Senior Step – fall prevention research for healthy ageing publication

Kim Bongers PhD at the department of geriatric medicine, Radboud University Medical Center, Nijmegen, The Netherlands is in the last phases of her research about the predictive value of gait speed and maximum step length for falling in community-dwelling older persons. This is part of  a bigger theme: fall prevention research for healthy ageing. 2M Engineering supported her research with the development and production of a solution to fully automatic monitor once a week walking speed in peoples homes. Although the results were different from what we had hoped for, it was still a showcase of how 2M Engineering can support research activities with quick prototyping, product development and followup in the field.


Background: falling is a major health problem.
Objective: to investigate the predictive value for falls of the maximum step length and gait speed.
Design: a prospective cohort study.
Setting: geriatric outpatient clinic.
Subjects: three hundred and fifty-two community-dwelling older persons screened by their general practitioner.
Methods: maximum step length and gait speed were recorded as part of a comprehensive geriatric assessment. One-year
follow-up was performed using the fall telephone system.
Results: one hundred and thirty-six (39%) of all subjects (mean age: 76.2 years, standard deviation: 4.3, 55% female), fell at
least once, of whom 96 were injured. Predictive values for any falls of both maximum step length and gait speed were low (area
under the curve (AUC): 0.53 and 0.50) and slightly better for recurrent falls (maximum step length AUC: 0.64 and gait speed
AUC: 0.59). After adding age, gender and fall history to the prediction model, the AUC was 0.63 for maximum step length
and 0.64 for gait speed, and for recurrent falls, the AUC was 0.69 both for maximum step length and gait speed. The prediction
of fall-related injuries showed similar results. A higher maximum step length score indicated a lower likelihood for falls
(hazards ratio 0.36; 95% confidence interval 0.17–0.78).
Conclusions: maximum step length and gait speed as single-item tools do not have sufficient power to predict future falls in
community-dwelling older persons.
Keywords: fall prediction, maximum step length, gait speed, mobility, older people


Age Ageing-2014-Bongers-ageing_afu151