Development of remote healthcare system for measuring and promoting healthy lifestyle

Youm, Sekyoung; Lee, Go-Eun; Park, Seunghun; Zhu, Weimo

doi:10.1016/j.eswa.2010.08.075

Cited by 23 publications

(12 citation statements)

References 3 publications

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“…Healthcare research in wearable devices has been constantly growing in various areas like remote patient monitoring and healthcare (66)(67)(68)(69)(70), wearable sensor-based systems for health monitoring (71)(72)(73)(74), and ambient-assisted living tools for older adults (75). For the older adults requiring continuous health monitoring, sensor-based wearables and remote monitoring will help eliminate the hassle of periodic commute to diagnostic centers, reduce the amount of recurring admissions to the hospital, and facilitate more efficient clinical visits with objective results (76).…”

Section: Discussionmentioning

confidence: 99%

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

Kumar

Toosizadeh

Mohler

et al. 2020

Preprint

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Background: Frailty is a highly recognized geriatric syndrome resulting in decline in reserve across multiple physiological systems. Impaired physical function is one of the major indicators of frailty.Objective: The goal of this study was to evaluate an algorithm that discriminates between frailty groups (non-frail and pre-frail/frail) based on gait performance parameters derived from unsupervised daily physical activity (DPA).Methods: DPA was acquired for 48 hours from older adults (≥65 years) using a tri-axial accelerometer motion-sensor. Continuous bouts of walking for 20s, 30s, 40s, 50s and 60s without pauses were identified from acceleration data. These were then used to extract qualitative measures (gait variability, gait asymmetry, and gait irregularity) and quantitative measures (total continuous walking duration and maximum number of continuous steps) to characterize gait performance. Association between frailty and gait performance parameters was assessed using multinomial logistic models with frailty as the dependent variable, and gait performance parameters along with demographic parameters as independent variables.Results: 126 older adults (44 non-frail, 60 pre-frail, and 22 frail, based on the Fried index) were recruited.Step-and stride-times, frequency domain gait variability, and continuous walking quantitative measures were significantly different between non-frail and pre-frail/frail groups (p<0.05). Among the five different durations (20s, 30s, 40s, 50s and 60s), gait performance parameters extracted from 60s continuous walks provided the best frailty assessment results. Using the 60s gait performance parameters in the logistic model, pre-frail/frail group (vs. non-frail) was identified with 76.8% sensitivity and 80% specificity.Discussion: Everyday walking characteristics were found to be associated with frailty. Along with quantitative measures of physical activity, qualitative measures are critical elements representing the early stages of frailty. In-home gait assessment offers an opportunity to screen for and monitor frailty.

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Section: Discussionmentioning

confidence: 99%

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

Kumar

Toosizadeh

Mohler

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Healthcare research in wearable devices has been constantly growing in various areas like remote patient monitoring and healthcare (69)(70)(71)(72)(73), wearable sensor-based systems for health monitoring (74)(75)(76)(77), and ambientassisted living tools for older adults (78). For the older adults requiring continuous health monitoring, sensorbased wearables and remote monitoring will help eliminate the hassle of periodic commute to diagnostic centers, reduce the amount of recurring admissions to the hospital, and facilitate more efficient clinical visits with objective results (79).…”

Section: Discussionmentioning

confidence: 99%

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

Kumar

Toosizadeh²,

Mohler

et al. 2020

Preprint

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Background: Frailty is a highly recognized geriatric syndrome resulting in decline in reserve across multiple physiological systems. Impaired physical function is one of the major indicators of frailty. The goal of this study was to evaluate an algorithm that discriminates between frailty groups (non-frail and pre-frail/frail) based on gait performance parameters derived from unsupervised daily physical activity (DPA). Methods: DPA was acquired for 48 hours from older adults (≥65 years) using a tri-axial accelerometer motion-sensor. Continuous bouts of walking for 20s, 30s, 40s, 50s and 60s without pauses were identified from acceleration data. These were then used to extract qualitative measures (gait variability, gait asymmetry, and gait irregularity) and quantitative measures (total continuous walking duration and maximum number of continuous steps) to characterize gait performance. Association between frailty and gait performance parameters was assessed using multinomial logistic models with frailty as the dependent variable, and gait performance parameters along with demographic parameters as independent variables. Results: 126 older adults (44 non-frail, 60 pre-frail, and 22 frail, based on the Fried index) were recruited. Step- and stride-times, frequency domain gait variability, and continuous walking quantitative measures were significantly different between non-frail and pre-frail/frail groups (p<0.05). Among the five different durations (20s, 30s, 40s, 50s and 60s), gait performance parameters extracted from 60s continuous walks provided the best frailty assessment results. Using the 60s gait performance parameters in the logistic model, pre-frail/frail group (vs. non-frail) was identified with 76.8% sensitivity and 80% specificity. Discussion: Everyday walking characteristics were found to be associated with frailty. Along with quantitative measures of physical activity, qualitative measures are critical elements representing the early stages of frailty. In-home gait assessment offers an opportunity to screen for and monitor frailty.Trial registration: The clinical trial was retrospectively registered on June 18th, 2013 with ClinicalTrials.gov, identifier NCT01880229.

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“…Recent papers suggested the use of ANNs in evaluating implementation model of remote health observing based on suitable questionnaire (Huang, 2010) and implementing web-based health monitoring systems for spread use by patients in their home environment (Youm, 2011).…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

The Use of Artificial Neural Networks in Clinical Medicine

Jovanović¹,

Milenković²,

Perković³

et al. 2016

Proceedings of the International Scientific Conference - Sinteza 2016

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Digital Agenda in Serbia involves the introduction of an electronic system for monitoring of the main characteristics of patients, disease progression and treatment outcomes through EHR (Electronic Health Record). Internationally standardized data set contains more than 150 variables, with a tendency to introduce new frequently. In addition to the increased demand for treatment, there are also demands for optimizing the health care system. In order to predict the likelihood of diagnosis, course and outcome of treatment, classically multivariate regression linear logistic model is being used. In recent years, studies indicate that the use of Artificial Neural Networks (ANN) may provide improved results in terms of likelihood of final diagnosis and outcomes that include input variables which, by their nature, have a non-linear interdependence. We reviewed current ANN models, their advantages and disadvantages compared to common regression models and their applicability in clinical practice. Also, we analyzed and suggested models that could possibly optimize the process of diagnosis, predict the cost and duration of treatment and rationalize medical and other resources by reducing the cost/ benefit coefficient per patient.

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Development of remote healthcare system for measuring and promoting healthy lifestyle

Cited by 23 publications

References 3 publications

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

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

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

The Use of Artificial Neural Networks in Clinical Medicine

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