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Factors Influencing Walking and Exercise Adherence in Healthy Older Adults Using Monitoring and Interfacing Technology: Preliminary Evidence

Background: Monitoring and interfacing technologies may increase physical activity (PA) program adherence in older adults, but they should account for aspects influencing older adults’ PA behavior. This study aimed at gathering preliminary wrist-based PA adherence data in free-living and relate thes...

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Detalles Bibliográficos
Autores principales: Albergoni, Andrea, Hettinga, Florentina J., Stut, Wim, Sartor, Francesco
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7503601/
https://www.ncbi.nlm.nih.gov/pubmed/32846988
http://dx.doi.org/10.3390/ijerph17176142
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author Albergoni, Andrea
Hettinga, Florentina J.
Stut, Wim
Sartor, Francesco
author_facet Albergoni, Andrea
Hettinga, Florentina J.
Stut, Wim
Sartor, Francesco
author_sort Albergoni, Andrea
collection PubMed
description Background: Monitoring and interfacing technologies may increase physical activity (PA) program adherence in older adults, but they should account for aspects influencing older adults’ PA behavior. This study aimed at gathering preliminary wrist-based PA adherence data in free-living and relate these to the influencing factors. Methods: Ten healthy older adults (4 females, aged 70–78 years) provided health, fatigue, activity levels, attitude towards pacing, and self-efficacy information and performed a 6 min-walk test to assess their fitness. After a baseline week they followed a two-week walking and exercise intervention. Participants saw their progress via a purposely designed mobile application. Results: Walking and exercise adherence did not increase during the intervention (p = 0.38, p = 0.65). Self-efficacy decreased (p = 0.024). The baseline physical component of the Short Form Health Survey was the most predictive variable of walking adherence. Baseline perceived risk of over-activity and resting heart rate (HR(rest)) were the most predictive variables of exercise adherence. When the latter two were used to cluster participants according to their exercise adherence, the fitness gap between exercise-adherent and non-adherent increased after the intervention (p = 0.004). Conclusions: Risk of over-activity and HR(rest) profiled short-term exercise adherence in older adults. If confirmed in a larger and longer study, these could personalize interventions aimed at increasing adherence.
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spelling pubmed-75036012020-09-27 Factors Influencing Walking and Exercise Adherence in Healthy Older Adults Using Monitoring and Interfacing Technology: Preliminary Evidence Albergoni, Andrea Hettinga, Florentina J. Stut, Wim Sartor, Francesco Int J Environ Res Public Health Article Background: Monitoring and interfacing technologies may increase physical activity (PA) program adherence in older adults, but they should account for aspects influencing older adults’ PA behavior. This study aimed at gathering preliminary wrist-based PA adherence data in free-living and relate these to the influencing factors. Methods: Ten healthy older adults (4 females, aged 70–78 years) provided health, fatigue, activity levels, attitude towards pacing, and self-efficacy information and performed a 6 min-walk test to assess their fitness. After a baseline week they followed a two-week walking and exercise intervention. Participants saw their progress via a purposely designed mobile application. Results: Walking and exercise adherence did not increase during the intervention (p = 0.38, p = 0.65). Self-efficacy decreased (p = 0.024). The baseline physical component of the Short Form Health Survey was the most predictive variable of walking adherence. Baseline perceived risk of over-activity and resting heart rate (HR(rest)) were the most predictive variables of exercise adherence. When the latter two were used to cluster participants according to their exercise adherence, the fitness gap between exercise-adherent and non-adherent increased after the intervention (p = 0.004). Conclusions: Risk of over-activity and HR(rest) profiled short-term exercise adherence in older adults. If confirmed in a larger and longer study, these could personalize interventions aimed at increasing adherence. MDPI 2020-08-24 2020-09 /pmc/articles/PMC7503601/ /pubmed/32846988 http://dx.doi.org/10.3390/ijerph17176142 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Albergoni, Andrea
Hettinga, Florentina J.
Stut, Wim
Sartor, Francesco
Factors Influencing Walking and Exercise Adherence in Healthy Older Adults Using Monitoring and Interfacing Technology: Preliminary Evidence
title Factors Influencing Walking and Exercise Adherence in Healthy Older Adults Using Monitoring and Interfacing Technology: Preliminary Evidence
title_full Factors Influencing Walking and Exercise Adherence in Healthy Older Adults Using Monitoring and Interfacing Technology: Preliminary Evidence
title_fullStr Factors Influencing Walking and Exercise Adherence in Healthy Older Adults Using Monitoring and Interfacing Technology: Preliminary Evidence
title_full_unstemmed Factors Influencing Walking and Exercise Adherence in Healthy Older Adults Using Monitoring and Interfacing Technology: Preliminary Evidence
title_short Factors Influencing Walking and Exercise Adherence in Healthy Older Adults Using Monitoring and Interfacing Technology: Preliminary Evidence
title_sort factors influencing walking and exercise adherence in healthy older adults using monitoring and interfacing technology: preliminary evidence
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7503601/
https://www.ncbi.nlm.nih.gov/pubmed/32846988
http://dx.doi.org/10.3390/ijerph17176142
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