Systematic causality mapping of factors leading to accidental falls of older adults

Zhu, Hongli; Hu, Kongxin; Liu, Shiyong; Kim, Ho-Cheol; Wang, Youfa; Xue, Qian‐Li

doi:10.1016/j.puhip.2020.100045

Cited by 4 publications

(3 citation statements)

References 46 publications

(50 reference statements)

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“…Diseases such as arthritis, Parkinson's disease, dementia and stroke represent additional factors that can lead to falls, and may be considered potentially modifiable where a treatment pathway exists [ 3 ]. Of these, environmental hazards and some types of medication are considered major risk factors leading to falls in older adults [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Deprescribing Medications that Increase the Risk of Falls in Older People: Exploring Doctors' Perspectives Using the Theoretical Domains Framework (TDF)

et al. 2022

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Background Falls can lead to hospitalisation and death in older people. Polypharmacy is a major risk factor, and deprescribing fall-risk increasing drugs (FRIDs) is one of several possible important preventive measures. The objective of this study was to explore the factors that influence doctors when deprescribing FRIDs in a hospital setting. Method Semi-structured interviews were conducted with consultant geriatricians and hospital doctors experienced in dealing with patients aged 65 years or older, at a large academic teaching hospital (~ 1000 beds), Dublin, Ireland. The interviews were directed by an interview guide and audio recorded and transcribed verbatim, with subsequent thematic analysis in NVivo 12 software. Results A total of 18 participants were interviewed. Barriers to deprescribing included: insufficient time, incomplete patient records, changing medications initiated by other specialists and difficulties following up patients after discharge. Facilitators included: enhanced documentation through electronic patient records, the support of other healthcare professionals such as clinical pharmacists, and patients’ engagement, which is considered essential for the success of the deprescribing process’s outcome. Conclusion Deprescribing FRIDs in older adults in the hospital setting is challenging. Implementation of the process in practice requires combined effort from stakeholders to tackle everyday work environment challenges. Future studies are required examining the clinical effect of the suggested interventions and exploring patients' involvement in deprescribing decisions. Supplementary Information The online version contains supplementary material available at 10.1007/s40266-022-00985-4.

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Section: Introductionmentioning

confidence: 99%

Deprescribing Medications that Increase the Risk of Falls in Older People: Exploring Doctors' Perspectives Using the Theoretical Domains Framework (TDF)

et al. 2022

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“…A decline in motor performance and physical activity are major risk factors for falls in older adults [ 9 , 10 , 11 , 12 ]. However, few studies explored the changes in physical activities and motor performance post a fall incident in older adults, but the reported observations are often limited to self-report of physical activities, used cross-sectional study design, lack data from age-matched non-fallers, and/or had limited sample size (less than 100 subjects) [ 13 , 14 , 15 , 16 , 17 ].…”

Section: Introductionmentioning

confidence: 99%

Association between Fall History and Gait, Balance, Physical Activity, Depression, Fear of Falling, and Motor Capacity: A 6-Month Follow-Up Study

Park

Atique

Mishra

et al. 2022

IJERPH

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Maintaining function in older adults is key to the quality of life and longevity. This study examined the potential impact of falls on accelerating further deterioration over time in gait, balance, physical activity, depression, fear of falling, and motor capacity in older adults. 163 ambulatory older adults (age = 76.5 ± 7.7 years) participated and were followed for 6 months. They were classified into fallers or non-fallers based on a history of falling within the past year. At baseline and 6 months, all participants were objectively assessed for gait, balance, and physical activity using wearable sensors. Additional assessments included psychosocial concerns (depression and fear of falling) and motor capacity (Timed Up and Go test). The fallers showed lower gait performance, less physical activity, lower depression level, higher fear of falling, and less motor capacity than non-fallers at baseline and 6-month follow-up. Results also revealed acceleration in physical activity and motor capacity decline compared to non-fallers at a 6-month follow-up. Our findings suggest that falls would accelerate deterioration in both physical activity and motor performance and highlight the need for effective therapy to reduce the consequences of falls in older adults.

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“…In clinical applications, machine learning algorithms are proving to be very useful ( Bao et al, 2019 ; Eichler et al, 2022 ). Machine learning (ML) (a) is a type of artificial intelligence (AI) focused on building computer systems that learn from data, (b) is a powerful tool for solving problems, streamlining various complex operations, and automating tasks, and (c) has broad applications in many areas, for example, science, engineering, industry, economics, databases, healthcare, and medicine ( Michalski et al, 2013 ; Alpaydin, 2016 ; Zhu et al, 2020 ; Sarker, 2021 ; Singh et al, 2021 ; Barton et al, 2024 ; Haimovich et al, 2024 ; Khalid et al, 2024 ). ML offers a wide range of techniques, such as decision trees, rule induction, neural networks, support vector machines (SVMs), clustering and classification methods, association rules, feature selection procedures, visualization, graphical models, or genetic algorithms; which are many more complex and use techniques well beyond traditional statistical techniques [i.e., hypothesis testing, experimental design, ANOVA, linear/logistic regression, generalized linear model (GLM), or principal component analysis (PCA)] ( Mitchel, 1997 ; Ben-David and Shalev-Shwartz, 2014 ; Marsland, 2015 ; Arnold et al, 2019 ; Bradley and Trevor, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

A machine learning approach to evaluate the impact of virtual balance/cognitive training on fall risk in older women

Sokołowska,

Świderski,

Smolis-Bąk

et al. 2024

Front. Comput. Neurosci.

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IntroductionNovel technologies based on virtual reality (VR) are creating attractive virtual environments with high ecological value, used both in basic/clinical neuroscience and modern medical practice. The study aimed to evaluate the effects of VR-based training in an elderly population.Materials and methodsThe study included 36 women over the age of 60, who were randomly divided into two groups subjected to balance-strength and balance-cognitive training. The research applied both conventional clinical tests, such as (a) the Timed Up and Go test, (b) the five-times sit-to-stand test, and (c) the posturographic exam with the Romberg test with eyes open and closed. Training in both groups was conducted for 10 sessions and embraced exercises on a bicycle ergometer and exercises using non-immersive VR created by the ActivLife platform. Machine learning methods with a k-nearest neighbors classifier, which are very effective and popular, were proposed to statistically evaluate the differences in training effects in the two groups.Results and conclusionThe study showed that training using VR brought beneficial improvement in clinical tests and changes in the pattern of posturographic trajectories were observed. An important finding of the research was a statistically significant reduction in the risk of falls in the study population. The use of virtual environments in exercise/training has great potential in promoting healthy aging and preventing balance loss and falls among seniors.

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Systematic causality mapping of factors leading to accidental falls of older adults

Cited by 4 publications

References 46 publications

Deprescribing Medications that Increase the Risk of Falls in Older People: Exploring Doctors' Perspectives Using the Theoretical Domains Framework (TDF)

Deprescribing Medications that Increase the Risk of Falls in Older People: Exploring Doctors' Perspectives Using the Theoretical Domains Framework (TDF)

Association between Fall History and Gait, Balance, Physical Activity, Depression, Fear of Falling, and Motor Capacity: A 6-Month Follow-Up Study

A machine learning approach to evaluate the impact of virtual balance/cognitive training on fall risk in older women

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