M
Mirjam Pijnappels
Researcher at VU University Amsterdam
Publications - 146
Citations - 6097
Mirjam Pijnappels is an academic researcher from VU University Amsterdam. The author has contributed to research in topics: Poison control & Gait (human). The author has an hindex of 38, co-authored 135 publications receiving 5002 citations. Previous affiliations of Mirjam Pijnappels include Radboud University Nijmegen & University of Amsterdam.
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Journal ArticleDOI
Identification of elderly fallers by muscle strength measures
TL;DR: Results indicate that whole leg extension strength is associated with the ability to prevent a fall after a gait perturbation and might be used to identify the elderly at risk of falling.
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Push-off reactions in recovery after tripping discriminate young subjects, older non-fallers and older fallers
TL;DR: The contribution of the support limb to prevent a fall after tripping is decreased in older adults, and lower limb strength could be an underlying factor and strength training might help to reduce fall risk.
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Gait stability and variability measures show effects of impaired cognition and dual tasking in frail people
Claudine J. C. Lamoth,Floor J. van Deudekom,Jos P. C. M. van Campen,Bregje A. Appels,Oscar J. de Vries,Mirjam Pijnappels +5 more
TL;DR: The results support the concept that changes in cognitive functions contribute to changes in the variability and stability of the gait pattern and might help to identify those elderly who are able to adapt walking ability and those who are not and thus are at greater risk for falling.
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Tripping without falling; lower limb strength, a limitation for balance recovery and a target for training in the elderly.
TL;DR: It can be concluded that transfer of resistance training effects to balance recovery is feasible, and high-risk fallers could be identified based on maximum leg press push-off force capacity.
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Ambulatory Fall-Risk Assessment: Amount and Quality of Daily-Life Gait Predict Falls in Older Adults
Kimberley S. van Schooten,Mirjam Pijnappels,Sietse M. Rispens,Petra J. M. Elders,Paul Lips,Jaap H. van Dieën +5 more
TL;DR: Daily-life accelerometry contributes substantially to the identification of individuals at risk of falls, and can predict falls in 6 months with good accuracy, according to retrospective and prospective analyses.