SynthesisAge and ageing2024
A systematic review of fall prediction models for community-dwelling older adults: comparison between models based on research cohorts and models based on routinely collected data.
Synthesis in Age and ageing, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
20 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Machine Learning and Deep Learning Models for Predicting Future Falls in Community-Dwelling Older Adults: Systematic Review and Meta-Analysis of Longitudinal Evidence.Journal of medical Internet research · 2026Pooled it
- A Systematic Review of Multivariate Models for Predicting Fall-Related Injuries in Older Adults.Journal of nursing management · 2026Pooled it
- Explainable AI reveals temporal risk pathways in fall prediction: Extracting clinical insights from multi-horizon machine learning models.GeroScience · 2026Article
- A Multidomain Longitudinal Analysis of Frailty, Functional Limitation, Balance and Falls in Older Adults.Healthcare (Basel, Switzerland) · 2026Article
- Multi-trajectory patterns of ADL, cognition, and depression with fall risk: evidence from a longitudinal study in China.BMC geriatrics · 2026Article
- Functional reserve mitigates cognitive-motor dual-task costs in older adults: insights from age, cohort, and behavioural strategies.European review of aging and physical activity : official journal of the European Group for Research into Elderly and Physical Activity · 2026Article
- Comparison and validation of machine learning models to predict 5-year fall risk among community-dwelling older adults in China.BMC geriatrics · 2026Article
- Comparison and validation of machine learning models to predict 5-year fall risk among community-dwelling older adults in China.BMC geriatrics · 2026Article
- Development of fall prediction models in community-dwelling older adults: comparison of biological and multidomain models for falls and injurious falls.BMC geriatrics · 2026Article
- The interplay of risk factors for future falls and fall-related injuries among community-dwelling older adults: a Bayesian network analysis.BMC public health · 2026Article
- Fall Prevention and Geriatric Nursing.Healthcare (Basel, Switzerland) · 2026Article
- Using data and artificial intelligence to improve care pathways of older people experiencing falls and frailty: Opportunities, challenges and practical considerations for clinicians.Clinical medicine (London, England) · 2026Review
- Primary healthcare professionals' perspectives on mobility limitation assessment and predictive model optimization for community-dwelling older adults: a qualitative research.Frontiers in public health · 2026Article
- Predicting first falls among older adults with chronic conditions and polypharmacy using routinely collected health records.Frontiers in artificial intelligence · 2026Article
- Association and predictive value analysis for mobility assessments and concerns about falling on falls in community-dwelling older adults: a prospective cohort study in China.BMC geriatrics · 2025Article
- Multifactorial predictors of falls in older adults: a decade of data from the National Health and Aging Trends Study.BMC geriatrics · 2025Article
- Review
- A novel electronic-health-record based, machine-learning model to predict 1-year risk of fall hospitalisation in older adults: a Hong Kong territory-wide cohort and modelling study.Age and ageing · 2025Article
- Urban-rural disparities in fall risk among older Chinese adults: insights from machine learning-based predictive models.Frontiers in public health · 2025Article
- [Development of a fall prediction model for community-dwelling older adults in South Korea using machine learning: a secondary data analysis].Journal of Korean biological nursing science · 2024Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
backgroundPrediction models can identify fall-prone individuals. Prediction models can be based on either data from research cohorts (cohort-based) or routinely collected data (RCD-based). We review and compare cohort-based and RCD-based studies describing the development and/or validation of fall prediction models for community-dwelling older adults.
methodsMedline and Embase were searched via Ovid until January 2023. We included studies describing the development or validation of multivariable prediction models of falls in older adults (60+). Both risk of bias and reporting quality were assessed using the PROBAST and TRIPOD, respectively.
resultsWe included and reviewed 28 relevant studies, describing 30 prediction models (23 cohort-based and 7 RCD-based), and external validation of two existing models (one cohort-based and one RCD-based). The median sample sizes for cohort-based and RCD-based studies were 1365 [interquartile range (IQR) 426-2766] versus 90 441 (IQR 56 442-128 157), and the ranges of fall rates were 5.4% to 60.4% versus 1.6% to 13.1%, respectively. Discrimination performance was comparable between cohort-based and RCD-based models, with the respective area under the receiver operating characteristic curves ranging from 0.65 to 0.88 versus 0.71 to 0.81. The median number of predictors in cohort-based final models was 6 (IQR 5-11); for RCD-based models, it was 16 (IQR 11-26). All but one cohort-based model had high bias risks, primarily due to deficiencies in statistical analysis and outcome determination.
conclusionsCohort-based models to predict falls in older adults in the community are plentiful. RCD-based models are yet in their infancy but provide comparable predictive performance with no additional data collection efforts. Future studies should focus on methodological and reporting quality.
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