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<p>Falls commonly occur in home environments, where conditions can increase fall risk. Identification and mitigation of environmental hazards are critical components of fall prevention. However, artificial intelligence (AI)-based fall prediction models have largely focused on individual-level predictors, with limited attention to home environmental hazards despite their modifiable role in fall risk. To address this knowledge gap, this study aimed to systematically review how environmental factors are incorporated into existing AI-based fall risk prediction models and summarize reported AI approaches and model performance among community-dwelling older adults. Following PRISMA guidelines, six electronic databases (PubMed, Embase, CINAHL, Cochrane Library, Web of Science, and Scopus) were searched through December 2025. Eligible studies applied AI-based models to predict falls among older adults in community settings and incorporated environmental factors as model inputs. Of 12,842 records screened, nine studies met the final inclusion criteria. Six used supervised machine learning with structured data, while three employed computer vision or robotics-based approaches. Environmental factors were heterogeneously represented, ranging from checklist-based indicators to sensor- and vision-derived measures. When included, environmental features contributed additional information in some studies, either by improving discrimination or by identifying actionable home hazards (AUC-ROC ranged from 0.67 to 0.76). Our findings showed that, although environmental hazards are established and modifiable contributors to fall risk, their distinct role within AI-based prediction models for community-dwelling older adults remains to be clearly defined. Greater integration of standardized and context-aware environmental information may enhance the relevance and preventive utility of AI-based fall risk prediction in community settings.</p>

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A systematic review of environmental factors in AI-based fall risk prediction models for community-dwelling older adults

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年8月2日

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  • Jiyoun Song,
  • Boeun Kim,
  • Min-Jeoung Kang,
  • Shuxuan Li,
  • Lingjie Liu,
  • Wonkyung Jung

摘要

Falls commonly occur in home environments, where conditions can increase fall risk. Identification and mitigation of environmental hazards are critical components of fall prevention. However, artificial intelligence (AI)-based fall prediction models have largely focused on individual-level predictors, with limited attention to home environmental hazards despite their modifiable role in fall risk. To address this knowledge gap, this study aimed to systematically review how environmental factors are incorporated into existing AI-based fall risk prediction models and summarize reported AI approaches and model performance among community-dwelling older adults. Following PRISMA guidelines, six electronic databases (PubMed, Embase, CINAHL, Cochrane Library, Web of Science, and Scopus) were searched through December 2025. Eligible studies applied AI-based models to predict falls among older adults in community settings and incorporated environmental factors as model inputs. Of 12,842 records screened, nine studies met the final inclusion criteria. Six used supervised machine learning with structured data, while three employed computer vision or robotics-based approaches. Environmental factors were heterogeneously represented, ranging from checklist-based indicators to sensor- and vision-derived measures. When included, environmental features contributed additional information in some studies, either by improving discrimination or by identifying actionable home hazards (AUC-ROC ranged from 0.67 to 0.76). Our findings showed that, although environmental hazards are established and modifiable contributors to fall risk, their distinct role within AI-based prediction models for community-dwelling older adults remains to be clearly defined. Greater integration of standardized and context-aware environmental information may enhance the relevance and preventive utility of AI-based fall risk prediction in community settings.