Literature DB >> 26245213

Clinical frailty syndrome assessment using inertial sensors embedded in smartphones.

A Galán-Mercant1, A I Cuesta-Vargas.   

Abstract

The aim of this study was to identify the series of kinematic variables demonstrating the greatest precision in discriminating between the function of two groups of elderly persons (frail and non-frail) in the 10 m expanded timed up and go (ETUG) test using inertial sensors embedded in the iPhone 4(®). A cross-sectional study was conducted to identify the kinematic variables with the highest degree of precision in discriminating between the two groups. The predicted capability of the kinematic variables was evaluated using receiver operating characteristic curves. The sample comprised 30 participants over 65 years old, 14 frail and 16 non-frail, assessed for frailty syndrome using the Fried criteria. Acceleration variables discriminated between the participant groups in the study; specifically these were the peak negative acceleration variables for motion axes x, y and z. In terms of sensitivity, the values were greater than or equal to those for the variable traditionally used to discriminate in the ETUG test, namely time. The kinematic parameters obtained from the internal inertial sensors in the iPhone 4(®) are promising additions to the ETUG analysis. There are encouraging signs that the analyses of these parameters in the separate phases of the ETUG procedure offer the potential for improved discrimination between frail and non-frail individuals. However, further in-depth study is required to verify the findings.

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Mesh:

Year:  2015        PMID: 26245213     DOI: 10.1088/0967-3334/36/9/1929

Source DB:  PubMed          Journal:  Physiol Meas        ISSN: 0967-3334            Impact factor:   2.833


  13 in total

1.  Exploration of Confounding Due to Poor Health in an Accelerometer-Mortality Study.

Authors:  Charles E Matthews; Richard P Troiano; Elizabeth A Salerno; David Berrigan; Shreya B Patel; Eric J Shiroma; Pedro F Saint-Maurice
Journal:  Med Sci Sports Exerc       Date:  2020-12

2.  Convergent Validity of a Wearable Sensor System for Measuring Sub-Task Performance during the Timed Up-and-Go Test.

Authors:  James Beyea; Chris A McGibbon; Andrew Sexton; Jeremy Noble; Colleen O'Connell
Journal:  Sensors (Basel)       Date:  2017-04-23       Impact factor: 3.576

Review 3.  Is Technology Present in Frailty? Technology a Back-up Tool for Dealing with Frailty in the Elderly: A Systematic Review.

Authors:  Iranzu Mugueta-Aguinaga; Begonya Garcia-Zapirain
Journal:  Aging Dis       Date:  2017-04-01       Impact factor: 6.745

4.  Experimental Validation of Depth Cameras for the Parameterization of Functional Balance of Patients in Clinical Tests.

Authors:  Francisco-Ángel Moreno; José Antonio Merchán-Baeza; Manuel González-Sánchez; Javier González-Jiménez; Antonio I Cuesta-Vargas
Journal:  Sensors (Basel)       Date:  2017-02-22       Impact factor: 3.576

5.  Wearable Sensors and the Assessment of Frailty among Vulnerable Older Adults: An Observational Cohort Study.

Authors:  Javad Razjouyan; Aanand D Naik; Molly J Horstman; Mark E Kunik; Mona Amirmazaheri; He Zhou; Amir Sharafkhaneh; Bijan Najafi
Journal:  Sensors (Basel)       Date:  2018-04-26       Impact factor: 3.576

6.  Association between the instrumented timed up and go test and cognitive function, fear of falling and quality of life in community dwelling people with dementia.

Authors:  Jonathan M Williams; Samuel R Nyman
Journal:  J Frailty Sarcopenia Falls       Date:  2018-12-01

7.  Mobile Computing Technologies for Health and Mobility Assessment: Research Design and Results of the Timed Up and Go Test in Older Adults.

Authors:  Vasco Ponciano; Ivan Miguel Pires; Fernando Reinaldo Ribeiro; María Vanessa Villasana; Rute Crisóstomo; Maria Canavarro Teixeira; Eftim Zdravevski
Journal:  Sensors (Basel)       Date:  2020-06-19       Impact factor: 3.576

8.  Age Moderates Differences in Performance on the Instrumented Timed Up and Go Test Between People With Dementia and Their Informal Caregivers.

Authors:  Jonathan M Williams; Samuel R Nyman
Journal:  J Geriatr Phys Ther       Date:  2021 Jul-Sep 01       Impact factor: 3.381

9.  Toward Using Wearables to Remotely Monitor Cognitive Frailty in Community-Living Older Adults: An Observational Study.

Authors:  Javad Razjouyan; Bijan Najafi; Molly Horstman; Amir Sharafkhaneh; Mona Amirmazaheri; He Zhou; Mark E Kunik; Aanand Naik
Journal:  Sensors (Basel)       Date:  2020-04-14       Impact factor: 3.576

10.  Validation, Reliability, and Responsiveness Outcomes Of Kinematic Assessment With An RGB-D Camera To Analyze Movement In Subacute And Chronic Low Back Pain.

Authors:  Manuel Trinidad-Fernández; David Beckwée; Antonio Cuesta-Vargas; Manuel González-Sánchez; Francisco-Angel Moreno; Javier González-Jiménez; Erika Joos; Peter Vaes
Journal:  Sensors (Basel)       Date:  2020-01-27       Impact factor: 3.576

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