Prevention of hip fractures in older adults residing in long-term care facilities with a hip airbag: a retrospective pilot study

Nemeth, Banne; Kaaij, Marleen van der; Nelissen, Rob G H H; Wijnen, Jan-Kees van; Drost, Katja T.; Blauw, Gerard J.

doi:10.1186/s12877-022-03221-1

Cited by 4 publications

(3 citation statements)

References 23 publications

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“…In particular, our results show that by combining mathematically generated data with a limited amount of real data using transfer learning, the ability to forecast falls is increased significantly over that obtained with training using a small amount of available real data alone. Our approach resulted in a fall forecasting model that is 60%–70% reliable up to 2.35 s. This increase in forecasting times increases the time available for activating strategies to minimize the effects of a fall ( Nemeth et al, 2022 ; Márquez et al, 2023 ).…”

Section: Discussionmentioning

confidence: 98%

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Pole balancing on the fingertip: model-motivated machine learning forecasting of falls

Debnath,

Chang,

Bhandari

et al. 2024

Front. Physiol.

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Introduction: There is increasing interest in developing mathematical and computational models to forecast adverse events in physiological systems. Examples include falls, the onset of fatal cardiac arrhythmias, and adverse surgical outcomes. However, the dynamics of physiological systems are known to be exceedingly complex and perhaps even chaotic. Since no model can be perfect, it becomes important to understand how forecasting can be improved, especially when training data is limited. An adverse event that can be readily studied in the laboratory is the occurrence of stick falls when humans attempt to balance a stick on their fingertips. Over the last 20 years, this task has been extensively investigated experimentally, and presently detailed mathematical models are available.Methods: Here we use a long short-term memory (LTSM) deep learning network to forecast stick falls. We train this model to forecast stick falls in three ways: 1) using only data generated by the mathematical model (synthetic data), 2) using only stick balancing recordings of stick falls measured using high-speed motion capture measurements (human data), and 3) using transfer learning which combines a model trained using synthetic data plus a small amount of human balancing data.Results: We observe that the LTSM model is much more successful in forecasting a fall using synthetic data than it is in forecasting falls for models trained with limited available human data. However, with transfer learning, i.e., the LTSM model pre-trained with synthetic data and re-trained with a small amount of real human balancing data, the ability to forecast impending falls in human data is vastly improved. Indeed, it becomes possible to correctly forecast 60%–70% of real human stick falls up to 2.35 s in advance.Conclusion: These observations support the use of model-generated data and transfer learning techniques to improve the ability of computational models to forecast adverse physiological events.

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Section: Discussionmentioning

confidence: 98%

“…An important benchmark is how far in advance is it possible to forecast a fall? Clearly, the longer a fall can be forecasted ahead, the more time is available to activate strategies to mitigate the adverse effects of a fall ( Nemeth et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Pole balancing on the fingertip: model-motivated machine learning forecasting of falls

Debnath,

Chang,

Bhandari

et al. 2024

Front. Physiol.

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“…After publication of this article [ 1 ], the authors reported that the wrong figure appeared as Fig. 2 ; the figure should have appeared as shown below.…”

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confidence: 99%

Correction: Prevention of hip fractures in older adults residing in long-term care facilities with a hip airbag: a retrospective pilot study

et al. 2022

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Vitamin D and hip protectors in osteosarcopenia: a combined hip fracture preventing approach

Giustina,

Giustina

2024

Rev Endocr Metab Disord

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Osteosarcopenia is an emerging clinical condition highly prevalent in the older people. Affected subjects due to their intrinsic skeletal fragility and propensity to falls are at elevated risk of hip fractures which can increase morbidity and mortality. Strategies for attenuating the impact of predisposing factors on hip fractures are not yet well defined and should derive from multidisciplinary care and collaborations. Our aim was to narratively review available data on the preventive role of vitamin D and hip protectors on hip fractures in older patients with sarcopenia. Older subjects are at high risk of vitamin D deficiency and of falls due to several concomitant factors besides osteosarcopenia. Vitamin D protective actions against hip fractures may be mediated by both skeletal (increased mineralization) and extra-skeletal (reduced risk of falls) actions. Hip protectors may act downstream attenuating the effects of falls although their use is still not yet enough widespread due to the suboptimal compliance obtained by traditional hard devices. Concomitant use of vitamin D and hip protectors may represent an effective strategy in the prevention of hip fractures which need to be tested in ad hoc designed clinical trials.

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Prevention of hip fractures in older adults residing in long-term care facilities with a hip airbag: a retrospective pilot study

Cited by 4 publications

References 23 publications

Pole balancing on the fingertip: model-motivated machine learning forecasting of falls

Pole balancing on the fingertip: model-motivated machine learning forecasting of falls

Correction: Prevention of hip fractures in older adults residing in long-term care facilities with a hip airbag: a retrospective pilot study

Vitamin D and hip protectors in osteosarcopenia: a combined hip fracture preventing approach

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