Related Experiment Video
Updated: May 29, 2026
Cognitive Function and Upper Limb Rehabilitation Training Post-Stroke Using a Digital Occupational Training System
Published on: December 29, 2023
Prediction of falling among stroke patients in rehabilitation
Tina Baetens1, Alexandra De Kegel, Patrick Calders
1Rehabilitation Sciences and Physiotherapy Ghent, Campus Heymans (UZ) 1B3, De Pintelaan 195, Ghent University. tina.baetens@ugent.be
Objective:
To identify risk factors and predict falling in stroke patients. To determine the strength of general vs mobility screening for this prediction.
Design:
Prospective study.
Subjects:
Patients in the first 6 months after stroke.
Methods:
The following assessments were carried out: an interview concerning civil state and fall history, Mini-Mental State Examination, Geriatric Depression Scale, Falls Efficacy Scale (FES), Star Cancellation Task (SCT), Stroop test, Berg Balance Scale, Functional Ambulation Categories (FAC), Motricity Index, grip and quadriceps strength, Modified Ashworth Scale, Katz scale, and a 6-month fall follow-up.
Results:
Sixty-five patients were included for analysis. Thirty -eight (58.5%) reported falling. Risk factors were: being single (odds ratio (OR) 4.7; 95% confidence interval (95% CI) 1.2-18.3), SCT-time (OR 1.2; 95% CI 1.0-1.3), grip strength on unaffected side (US) (OR 0.1; 95% CI 0.0-0.8), FAC 3 vs FAC 4-5 (OR 8.1; 95% CI 1.5-43.2), and walking aid vs none (OR 5.1; 95% CI 1.4-17.8). These parameters were included in predictive models, which finally implied a general model (I) with inclusion of SCT-time, FAC category and use of walking aid. A mobility model (II) included: FAC category and strength (US). These models showed a sensitivity of 94.1% and 76.3%, respectively.
Conclusion:
Several assessments and both prediction models showed acceptable accuracy in identifying fall-prone patients. A purely physical model can be used; however, looking beyond mobility aspects adds value. Further validation of these results is required.
