Sensor-based Characterization of Daily Walking: A New Paradigm in Pre-frailty/Frailty Assessment

Kumar, Danya Pradeep; Toosizadeh, Nima; Mohler, Jane; Ehsani, Hossein; Mannier, Cassidy; Laksari, Kaveh

doi:10.21203/rs.2.16000/v2

Cited by 4 publications

(5 citation statements)

References 52 publications

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“…The hypotheses of the study were con rmed. The sensor technology we uses is quite complex in terms of the sophisticated data processing require due to larger number of sensors we used as compared to a study where a single wearable sensor was able to extract similar gait parameters (21). However, the application of multiple sensors allowed us determine not only temporal but also spatial biomechanical parameters like joint amplitudes during gait, which in turn may provide more quantitative parameters that may be sensitive to distinguishing frailty levels.…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, more accessible alternatives could be used, such as wearable sensors, which are easier to use, more practical, and require fewer resources. There is growing scienti c evidence that gait assessment using wearable sensors could be used for prefrailty and frailty screening (20)(21)(22)(23). Simple and easy-to-use sensors (such as those incorporated into a smartphone) could be used to investigate gait at home and offer advantages in frailty evaluations where the full application or interpretation of Fried criteria is impracticable.…”

Section: Introductionmentioning

confidence: 99%

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Wearable Sensors Technology as a Tool for Discriminating Frailty Levels During Instrumented Gait Analysis

Apsega¹,

Petrauskas

Alekna³

et al. 2020

Preprint

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Background: One of the greatest challenges facing the healthcare of the aging population is frailty. There is growing scientific evidence that gait assessment using wearable sensors could be used for prefrailty and frailty screening. The purpose of this study was to examine the ability of a wearable sensor-based assessment of gait to discriminate between frailty levels (robust, prefrail, and frail).Methods: 133 participants (≥ 60 years) were recruited and frailty was assessed using the Fried criteria. Gait was assessed using wireless inertial sensors attached by straps on the thighs, shins, and feet. Between-group differences in frailty were assessed using analysis of variance. Associations between frailty and gait parameters was assessed using multinomial logistic models with frailty as the dependent variable. We used receiver operating characteristic (ROC) curves to calculate the area under the curve (AUC) to estimate the predictive validity of each parameter. The cut-off values were calculated based on the Youden index.Results: Frailty was identified in 37 (28%) participants, prefrailty in 66 (50%), and no Fried criteria were found in 30 (23%) participants. Gait speed, stance phase time, swing phase time, stride time, double support time, and cadence were able to discriminate frailty from robust, and prefrail from robust. Stride time (AUC = 0.915), stance phase (AUC = 0.923), and cadence (AUC = 0.930) were the most sensitive parameters to separate frail or prefrail from robust. Other gait parameters, such as double support, had poor sensitivity. We determined the value of stride time (1.19s), stance phase time (0.68s), and cadence (101 steps/min) to identify individuals with prefrailty or frailty with sufficient sensitivity and specificity.Conclusions: The results of our study show that gait analysis using wearable sensors could discriminate between frailty levels. We were able to identify several gait indicators apart from gait speed that distinguish frail or prefrail from robust with sufficient sensitivity and specificity. If improved and adapted for everyday use, gait assessment technologies could contribute to frailty screening and monitoring.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Wearable Sensors Technology as a Tool for Discriminating Frailty Levels During Instrumented Gait Analysis

Apsega¹,

Petrauskas

Alekna³

et al. 2020

Preprint

View full text Add to dashboard Cite

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“…In terms of frailty, comparable studies report ROC AUC values between 0.72 and 0.86, based on wearable sensors [51][52][53] . These results were primarily obtained on the basis of gait and physical activity measures.…”

Section: Discussionmentioning

confidence: 99%

A systems approach towards remote health-monitoring in older adults: Introducing a zero-interaction digital exhaust

et al. 2022

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Using connected sensing devices to remotely monitor health is a promising way to help transition healthcare from a rather reactive to a more precision medicine oriented proactive approach, which could be particularly relevant in the face of rapid population ageing and the challenges it poses to healthcare systems. Sensor derived digital measures of health, such as digital biomarkers or digital clinical outcome assessments, may be used to monitor health status or the risk of adverse events like falls. Current research around such digital measures has largely focused on exploring the use of few individual measures obtained through mobile devices. However, especially for long-term applications in older adults, this choice of technology may not be ideal and could further add to the digital divide. Moreover, large-scale systems biology approaches, like genomics, have already proven beneficial in precision medicine, making it plausible that the same could also hold for remote-health monitoring. In this context, we introduce and describe a zero-interaction digital exhaust: a set of 1268 digital measures that cover large parts of a person’s activity, behavior and physiology. Making this approach more inclusive of older adults, we base this set entirely on contactless, zero-interaction sensing technologies. Applying the resulting digital exhaust to real-world data, we then demonstrate the possibility to create multiple ageing relevant digital clinical outcome assessments. Paired with modern machine learning, we find these assessments to be surprisingly powerful and often on-par with mobile approaches. Lastly, we highlight the possibility to discover novel digital biomarkers based on this large-scale approach.

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“…In terms of frailty, comparable studies report ROC AUC values between 0.72 and 0.86, based on wearable sensors [36,37,38]. These results were primarily obtained on the basis of gait and physical activity measures.…”

Section: Discussionmentioning

confidence: 99%

An aging focused unobtrusive and Privacy-Preserving Digital Behaviorome

Schuetz

Knobel

Botros

et al. 2021

Preprint

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Digital measures are increasingly used as objective health measures in remote-monitoring settings. In addition to their use in purely clinical research, such as in clinical trials, one promising application area for sensor-derived digital measures is in technology-assisted ageing and ageing-related research. In this context, digital measures may be used to measure the risk of certain adverse events such as falls, and also to provide novel research insights into ageing and ageing-related conditions, like cognitive impairment. While major emphasis has been placed on deriving one or more digital measures from wearable devices, a more holistic approach inspired by systems biology that leverages large, non-exhaustive sets of digital measures may prove highly beneficial. Such an approach would be useful if combined with modern big data approaches like machine learning. As such, extensive sets of digital measures, which may be referred to as digital behavioromes, could help characterise new phenotypes in deep phenotyping efforts. These measures could also assist in the discovery of novel digital biomarkers or in the creation of digital clinical outcome assessments. While clinical research into digital measures focuses primarily on measures derived from wearable devices, proven technology used for long-term remote monitoring of older adults is generally contactless, unobtrusive, and privacy-preserving. In this context, we introduce and describe a digital behaviorome: a large, non-exhaustive set of digital measures based entirely on contactless, unobtrusive, and privacy-preserving sensor technologies. We also demonstrate how such a behaviorome can be used to build digital clinical outcome assessments that are relevant to ageing and derived from machine learning. These outcomes included fall risk, frailty, mild cognitive impairment, and late-life depression. With the exception of late-life depression, all digital outcome assessments demonstrated a promising ability (ROC AUC ≥ 0.7) to discriminate between positive and negative health outcomes, often in the range of comparable work with wearable devices. Finally, we highlight the possibility of using these digital behaviorome-based outcome assessments to discover novel potential digital biomarkers for each outcome. Here, we found reasonable contributors but also some potentially interesting new candidates regarding fall risk and mild cognitive impairment.

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Sensor-based Characterization of Daily Walking: A New Paradigm in Pre-frailty/Frailty Assessment

Cited by 4 publications

References 52 publications

Wearable Sensors Technology as a Tool for Discriminating Frailty Levels During Instrumented Gait Analysis

Wearable Sensors Technology as a Tool for Discriminating Frailty Levels During Instrumented Gait Analysis

A systems approach towards remote health-monitoring in older adults: Introducing a zero-interaction digital exhaust

An aging focused unobtrusive and Privacy-Preserving Digital Behaviorome

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