Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA
August 6, 2026 · via Europe PMC
The incidence and predictors of falling in older adults: the results of follow-up in Iranian multicenter osteoporosis study (IMOS).
Ahmadi M, Sargolzaeimoghaddam M, Mohseni V, +7 more, Ostovar A.
AI summary — for doctors, generated from the abstract only
In 1,410 older Iranian adults followed for a year, a prior fall was the strongest independent predictor of further falls.
What was studied
A prospective cohort within the Iranian Multicenter Osteoporosis Study following community-dwelling adults aged ≥50 for 12 months with phone-based fall tracking and Cox regression.
Key findings
- Fall incidence 14.27 per 1,000 person-years (95% CI 12.64-16.09), higher in women
- One prior fall HR 2.49 (1.76-3.70) and two or more HR 2.55 (1.74-3.70)
- Visual impairment HR 1.48 (1.05-2.08)
- Any prior-year fall conferred 2.70-fold risk (1.99-3.66)
Clinical relevance
Supports routine fall-history assessment as part of fracture risk stratification.
Who should care
Limitations
Single-country cohort with falls ascertained by telephone self-report.
Asking about last year's falls is the simplest way to spot future fall risk.
This is an AI-generated summary of the published abstract, not a clinical recommendation. It may not reflect the full text or subsequent updates — verify against the original source before acting on it.
Abstract
In this prospective cohort of 1,410 community-dwelling Iranian older adults, a history of falls in the previous year was the strongest independent predictor of incident falls over one-year follow-up, supporting routine fall-history assessment as part of fracture-risk stratification.
Background
Osteoporosis and falls are closely interlinked, particularly among aging populations, leading to increased risks of fragility fractures and disability. Identifying fall-related risk factors is essential for guiding prevention strategies.
Objective
This study aimed to investigate the incidence of falls and associated risk factors among community-dwelling older adults in Iran over a one-year follow-up period.
Methods
Individuals aged ≥ 50 years, who participated in the Iranian Multicenter Osteoporosis Study were followed. Baseline data included demographics, medical history, functional assessments, and laboratory findings. Falls were tracked prospectively over 12 months through phone calls. Statistical analyses included chi-square tests for categorical variables, independent t-tests for numerical comparisons, and Cox proportional hazards regression to identify predictors of falls. Variables with p < 0.20 in univariable analyses were entered into the multivariable model.
Results
Among 1,450 participants at baseline, 1,410 (54.6% women; mean age 60.7 ± 8.0 years) completed follow-up. The overall fall incidence rate was 14.27 per 1,000 person-years (95% CI: 12.64-16.09), with higher rates among women than men. In unadjusted analyses, female sex, higher BMI, dizziness, visual impairment, and prior fall history were associated with increased fall risk. After multivariable adjustment, a history of one fall during past year (HR:2.49, 95%CI:1.76-3.70), two or more falls during past year (HR:2.55, 95%CI:1.74-3.70) and visual impairment (HR:1.48,95%CI:1.05-2.08) remained independent predictors of incident falls. Those with a fall in the previous year showed more than a 2.7-fold higher risk of subsequent falls (HR = 2.70; 95% CI: 1.99-3.66; p < 0.001).
Conclusion
History of previous falls was the important independent predictor of incident falls after adjustment, highlighting the importance of systematic fall-history assessment in community-dwelling older adults.
DOI
10.1007/s00198-026-08183-3
PMID
42562912
Research metadata (titles, abstracts, authors, publication dates) is sourced from Europe PMC, which aggregates content from PubMed/MEDLINE, PubMed Central, and other providers under the U.S. National Library of Medicine (NLM/NCBI). NLM does not claim copyright on abstracts; the original journal publishers or authors may hold rights to the full articles. AI-generated summaries are derived only from the abstract, never the full text, and are always labeled as AI-generated.
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