ArticleScientific reports2022
Exploratory analysis using machine learning of predictive factors for falls in type 2 diabetes.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 10 citations in OpenAlex.
- A scoping review of machine learning models to predict risk of falls in elders, without using sensor data.Diagnostic and prognostic research · 2025Review
- Longitudinal association of SGLT2 inhibitors and GLP-1RAs on falls in persons with type 2 diabetes.Scientific reports · 2025Article
- Balance Ability Characteristics and Related Factors in Athletes Across Different Sports: A Preliminary Study.Healthcare (Basel, Switzerland) · 2024Article
- New balance capability index as a screening tool for mild cognitive impairment.BMC geriatrics · 2023Article
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Authors and funding
11 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We aimed to investigate the status of falls and to identify important risk factors for falls in persons with type 2 diabetes (T2D) including the non-elderly. Participants were 316 persons with T2D who were assessed for medical history, laboratory data and physical capabilities during hospitalization and given a questionnaire on falls one year after discharge. Two different statistical models, logistic regression and random forest classifier, were used to identify the important predictors of falls. The response rate to the survey was 72%; of the 226 respondents, there were 129 males and 97 females (median age 62 years). The fall rate during the first year after discharge was 19%. Logistic regression revealed that knee extension strength, fasting C-peptide (F-CPR) level and dorsiflexion strength were independent predictors of falls. The random forest classifier placed grip strength, F-CPR, knee extension strength, dorsiflexion strength and proliferative diabetic retinopathy among the 5 most important variables for falls. Lower extremity muscle weakness, elevated F-CPR levels and reduced grip strength were shown to be important risk factors for falls in T2D. Analysis by random forest can identify new risk factors for falls in addition to logistic regression.
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Registered trials
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