scholarly journals Automated Loss-of-Balance Event Identification in Older Adults at Risk of Falls during Real-World Walking Using Wearable Inertial Measurement Units

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4661
Author(s):  
Jeremiah Hauth ◽  
Safa Jabri ◽  
Fahad Kamran ◽  
Eyoel W. Feleke ◽  
Kaleab Nigusie ◽  
...  

Loss-of-balance (LOB) events, such as trips and slips, are frequent among community-dwelling older adults and are an indicator of increased fall risk. In a preliminary study, eight community-dwelling older adults with a history of falls were asked to perform everyday tasks in the real world while donning a set of three inertial measurement sensors (IMUs) and report LOB events via a voice-recording device. Over 290 h of real-world kinematic data were collected and used to build and evaluate classification models to detect the occurrence of LOB events. Spatiotemporal gait metrics were calculated, and time stamps for when LOB events occurred were identified. Using these data and machine learning approaches, we built classifiers to detect LOB events. Through a leave-one-participant-out validation scheme, performance was assessed in terms of the area under the receiver operating characteristic curve (AUROC) and the area under the precision recall curve (AUPR). The best model achieved an AUROC ≥0.87 for every held-out participant and an AUPR 4-20 times the incidence rate of LOB events. Such models could be used to filter large datasets prior to manual classification by a trained healthcare provider. In this context, the models filtered out at least 65.7% of the data, while detecting ≥87.0% of events on average. Based on the demonstrated discriminative ability to separate LOBs and normal walking segments, such models could be applied retrospectively to track the occurrence of LOBs over an extended period of time.

2020 ◽  
Vol 7 ◽  
Author(s):  
Shirley Handelzalts ◽  
Neil B. Alexander ◽  
Nicholas Mastruserio ◽  
Linda V. Nyquist ◽  
Debra M. Strasburg ◽  
...  

Gerontology ◽  
2022 ◽  
pp. 1-10
Author(s):  
Danique J.J. van Gulick ◽  
Sander I.B. Perry ◽  
Marike van der Leeden ◽  
Jolan G.M. van Beek ◽  
Cees Lucas ◽  
...  

<b><i>Introduction:</i></b> Falls are a worldwide health problem among community-dwelling older adults. Emerging evidence suggests that foot problems increase the risk of falling, so the podiatrist may be crucial in detecting foot-related fall risk. However, there is no screening tool available which can be used in podiatry practice. The predictive value of existing tools is limited, and the implementation is poor. The development of risk models for specific clinical populations might increase the prediction accuracy and implementation. Therefore, the aim of this study was to develop and internally validate an easily applicable clinical prediction model (CPM) that can be used in podiatry practice to predict falls in community-dwelling older adults with foot (-related) problems. <b><i>Methods:</i></b> This was a prospective study including community-dwelling older adults (≥65 years) visiting podiatry practices. General fall-risk variables, and foot-related and function-related variables were considered as predictors for the occurrence of falls during the 12-month follow-up. Logistic regression analysis was used for model building, and internal validation was done by bootstrap resampling. <b><i>Results:</i></b> 407 participants were analyzed; the event rate was 33.4%. The final model included fall history in the previous year, unsteady while standing and walking, plantarflexor strength of the lesser toes, and gait speed. The area under the receiver operating characteristic curve was 0.71 (95% CI: 0.66–0.76) in the sample and estimated as 0.65 after shrinkage. <b><i>Conclusion:</i></b> A CPM based on fall history in the previous year, feeling unsteady while standing and walking, decreased plantarflexor strength of the lesser toes, and reduced gait speed has acceptable accuracy to predict falls in our sample of podiatry community-dwelling older adults and is easily applicable in this setting. The accuracy of the model in clinical practice should be demonstrated through external validation of the model in a next study.


2014 ◽  
Vol 44 (1) ◽  
pp. 109-115 ◽  
Author(s):  
Christopher P. Carty ◽  
Neil J. Cronin ◽  
Deanne Nicholson ◽  
Glen A. Lichtwark ◽  
Peter M. Mills ◽  
...  

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Satoshi Kurose ◽  
Satoru Nishikawa ◽  
Takayasu Nagaoka ◽  
Masahiro Kusaka ◽  
Jun Kawamura ◽  
...  

Abstract This study aimed to investigate risk factors for sarcopenia in community-dwelling older adults visiting regional medical institutions. We retrospectively analyzed medical records of 552 participants (mean age: 74.6 ± 6.7 years, males 31.3%) who underwent body composition evaluation between March 2017 and December 2018 at one of 24 medical institutions belonging to the Kadoma City Medical Association in Japan. We collected the participant’s characteristics and laboratory data. Sarcopenia was diagnosed according to the Asian Working Group for Sarcopenia 2019. Sarcopenia, including severe sarcopenia, was detected in 22.3% of all participants, 17.3% of men, and 24.5% of women; rates increased with age. Multivariate logistic regression analysis revealed age (odds ratio [OR]: 2.12; 95% confidence interval [CI] 1.20–3.75), obesity (OR: 0.15; 95% CI 0.07–0.32), hypertension (OR: 0.44; 95% CI 0.25–0.76), certification of long term care (OR: 3.32; 95% CI 1.41–7.81), number of daily conversations (OR: 0.44; 95% CI 0.25–0.77), and malnutrition (OR: 2.42; 95% CI 1.04–5.60) as independent predictors of sarcopenia. Receiver operating characteristic curve analysis demonstrated that the cut-off for daily conversations defining sarcopenia was 4.8 persons. The prevalence of sarcopenia in this study was 22.3%. Besides traditional risk factors for sarcopenia, the number of daily conversations was an independent factor.


2019 ◽  
Vol 48 (Supplement_4) ◽  
pp. iv18-iv27
Author(s):  
Jing Wen Goh ◽  
Devinder Kaur Ajit Singh ◽  
Suzana Shahar

Abstract Introduction Early falls screening among community dwelling older adults is important as a part of falls prevention strategy. Falls Screening Mobile Application (FallSA) was demonstrated to be accepted, reliable and valid to be used for self-risk assessment among community dwelling older adults in an earlier study. However, its discriminative ability is unknown. We aimed to examine the discriminative ability of FallSA in classifying fallers and non-fallers among community dwelling older adults. Methodology A total of 182 community dwelling older adults with mean age of 71.42 ± 5.1 participated in this cross sectional study. Participants demographic and falls history data were obtained. Participants with one or more falls were categorized as fallers. FallSA was used to identify participants falls risk. Independent t-test was used to compare falls risk score among fallers and non-fallers for its discriminative ability. Results Approximately 20% participants were categorized as fallers. Majority of the fallers were females (66.7%), had lower physical activity level and higher scores of geriatric depression scales compared to non-fallers. There was a significant (p&lt; 0.01) different in the FallSA score between fallers (7.33±1.77) and non-fallers (4.34±1.72). Conclusion Our study results showed that FallSA could be used to discriminate fallers and non-fallers in community dwelling older adults. Further studies are in progress to determine the predictive validity of FallSA.


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