Cargando…

Prescribing Personalized Exercise Programs Using Smartphone Sensors For Remote Fitness Assessment

Guidelines for physical activity emphasize multiple fitness components among people aged >65. The age-related increase in variability of fitness components necessitates accurate individualized assessment prior to optimal prescription for personalized exercise program. Accordingly, we tested feasi...

Descripción completa

Detalles Bibliográficos
Autores principales: Jacobs, Jeremy, Yekutiel, Ziv, Arnon, Mical, Argov, Esther, Karlinsky, Keren Tchelet, Benmoha, Eti, Netz, yael
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8681662/
http://dx.doi.org/10.1093/geroni/igab046.3256
_version_ 1784617030914146304
author Jacobs, Jeremy
Yekutiel, Ziv
Arnon, Mical
Argov, Esther
Karlinsky, Keren Tchelet
Benmoha, Eti
Netz, yael
author_facet Jacobs, Jeremy
Yekutiel, Ziv
Arnon, Mical
Argov, Esther
Karlinsky, Keren Tchelet
Benmoha, Eti
Netz, yael
author_sort Jacobs, Jeremy
collection PubMed
description Guidelines for physical activity emphasize multiple fitness components among people aged >65. The age-related increase in variability of fitness components necessitates accurate individualized assessment prior to optimal prescription for personalized exercise program. Accordingly, we tested feasibility and effectiveness of a novel tool designed to remotely assess balance, flexibility, and strength using smartphone sensors (accelerometer/gyroscope), and subsequently remotely deliver personalized exercise programs via smartphone. This pilot study enrolled 52 healthy volunteers (34 females) aged 65+, with normal cognition and low fall-risk. Baseline preliminary data from smartphone fitness assessment were analyzed to generate 42 fitness digital-markers, used to generate personalized exercise programs (5 times/week for 6 weeks). Programs included graded exercises for upper/lower body, flexibility, strength, and balance (dynamic, static, vestibular). Fitness was remotely assessed at baseline and after six weeks. Average age was 74.7±6.4 years; adherence was 3.6±1.7 exercise sessions/week. Significant improvement for pre/post testing was observed for 10/12 digital-markers of strength/flexibility for upper/lower body (sit-to-stand repetitions/duration; arm-lift duration; torso-rotation; arm-extension/flexion). Balance improved significantly for 6/10 measures of tandem-stance, with consistent (non-significant) trends observed across 20 balance digital-markers of tandem-walk and one leg-stance. Balance showed greatest improvement among the 37 participants exercising ≥3/week. These preliminary results serve as proof of concept among people aged >65: high adherence and improved fitness confirm the potential benefits and niche for remote fitness assessment used to generate personalized exercise programs. Future research is required to confirm the benefits among specific patient groups, such as those with frailty, deconditioning, cognitive and functional impairment.
format Online
Article
Text
id pubmed-8681662
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Oxford University Press
record_format MEDLINE/PubMed
spelling pubmed-86816622021-12-17 Prescribing Personalized Exercise Programs Using Smartphone Sensors For Remote Fitness Assessment Jacobs, Jeremy Yekutiel, Ziv Arnon, Mical Argov, Esther Karlinsky, Keren Tchelet Benmoha, Eti Netz, yael Innov Aging Abstracts Guidelines for physical activity emphasize multiple fitness components among people aged >65. The age-related increase in variability of fitness components necessitates accurate individualized assessment prior to optimal prescription for personalized exercise program. Accordingly, we tested feasibility and effectiveness of a novel tool designed to remotely assess balance, flexibility, and strength using smartphone sensors (accelerometer/gyroscope), and subsequently remotely deliver personalized exercise programs via smartphone. This pilot study enrolled 52 healthy volunteers (34 females) aged 65+, with normal cognition and low fall-risk. Baseline preliminary data from smartphone fitness assessment were analyzed to generate 42 fitness digital-markers, used to generate personalized exercise programs (5 times/week for 6 weeks). Programs included graded exercises for upper/lower body, flexibility, strength, and balance (dynamic, static, vestibular). Fitness was remotely assessed at baseline and after six weeks. Average age was 74.7±6.4 years; adherence was 3.6±1.7 exercise sessions/week. Significant improvement for pre/post testing was observed for 10/12 digital-markers of strength/flexibility for upper/lower body (sit-to-stand repetitions/duration; arm-lift duration; torso-rotation; arm-extension/flexion). Balance improved significantly for 6/10 measures of tandem-stance, with consistent (non-significant) trends observed across 20 balance digital-markers of tandem-walk and one leg-stance. Balance showed greatest improvement among the 37 participants exercising ≥3/week. These preliminary results serve as proof of concept among people aged >65: high adherence and improved fitness confirm the potential benefits and niche for remote fitness assessment used to generate personalized exercise programs. Future research is required to confirm the benefits among specific patient groups, such as those with frailty, deconditioning, cognitive and functional impairment. Oxford University Press 2021-12-17 /pmc/articles/PMC8681662/ http://dx.doi.org/10.1093/geroni/igab046.3256 Text en © The Author(s) 2021. Published by Oxford University Press on behalf of The Gerontological Society of America. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Abstracts
Jacobs, Jeremy
Yekutiel, Ziv
Arnon, Mical
Argov, Esther
Karlinsky, Keren Tchelet
Benmoha, Eti
Netz, yael
Prescribing Personalized Exercise Programs Using Smartphone Sensors For Remote Fitness Assessment
title Prescribing Personalized Exercise Programs Using Smartphone Sensors For Remote Fitness Assessment
title_full Prescribing Personalized Exercise Programs Using Smartphone Sensors For Remote Fitness Assessment
title_fullStr Prescribing Personalized Exercise Programs Using Smartphone Sensors For Remote Fitness Assessment
title_full_unstemmed Prescribing Personalized Exercise Programs Using Smartphone Sensors For Remote Fitness Assessment
title_short Prescribing Personalized Exercise Programs Using Smartphone Sensors For Remote Fitness Assessment
title_sort prescribing personalized exercise programs using smartphone sensors for remote fitness assessment
topic Abstracts
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8681662/
http://dx.doi.org/10.1093/geroni/igab046.3256
work_keys_str_mv AT jacobsjeremy prescribingpersonalizedexerciseprogramsusingsmartphonesensorsforremotefitnessassessment
AT yekutielziv prescribingpersonalizedexerciseprogramsusingsmartphonesensorsforremotefitnessassessment
AT arnonmical prescribingpersonalizedexerciseprogramsusingsmartphonesensorsforremotefitnessassessment
AT argovesther prescribingpersonalizedexerciseprogramsusingsmartphonesensorsforremotefitnessassessment
AT karlinskykerentchelet prescribingpersonalizedexerciseprogramsusingsmartphonesensorsforremotefitnessassessment
AT benmohaeti prescribingpersonalizedexerciseprogramsusingsmartphonesensorsforremotefitnessassessment
AT netzyael prescribingpersonalizedexerciseprogramsusingsmartphonesensorsforremotefitnessassessment