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Using Wearable Technology to Quantify Physical Activity Recovery: Secondary Report From the AFTER (App-Facilitated Tele-Rehabilitation) Program for COVID-19 Survivors Randomized Study

BACKGROUND: Knowledge on physical activity recovery after COVID-19 survival is limited. The AFTER (App-Facilitated Tele-Rehabilitation) program for COVID-19 survivors randomized participants, following hospital discharge, to either education and unstructured physical activity or a telerehabilitation...

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Autores principales: Churchill, Laura, Morrow, Mary, Capin, Jacob J, Jolley, Sarah E, Hare, Kristine, MaWhinney, Samantha, Stevens-Lapsley, Jennifer E, Erlandson, Kristine M
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10131697/
https://www.ncbi.nlm.nih.gov/pubmed/36939818
http://dx.doi.org/10.2196/43436
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author Churchill, Laura
Morrow, Mary
Capin, Jacob J
Jolley, Sarah E
Hare, Kristine
MaWhinney, Samantha
Stevens-Lapsley, Jennifer E
Erlandson, Kristine M
author_facet Churchill, Laura
Morrow, Mary
Capin, Jacob J
Jolley, Sarah E
Hare, Kristine
MaWhinney, Samantha
Stevens-Lapsley, Jennifer E
Erlandson, Kristine M
author_sort Churchill, Laura
collection PubMed
description BACKGROUND: Knowledge on physical activity recovery after COVID-19 survival is limited. The AFTER (App-Facilitated Tele-Rehabilitation) program for COVID-19 survivors randomized participants, following hospital discharge, to either education and unstructured physical activity or a telerehabilitation program. Step count data were collected as a secondary outcome, and we found no significant differences in total step count trajectories between groups at 6 weeks. Further step count data were not analyzed. OBJECTIVE: The purpose of this analysis was to examine step count trajectories and correlates among all participants (combined into a single group) across the 12-week study period. METHODS: Linear mixed models with random effects were used to model daily steps over the number of study days. Models with 0, 1, and 2 inflection points were considered, and the final model was selected based on the highest log-likelihood value. RESULTS: Participants included 44 adults (41 with available Fitbit [Fitbit LLC] data). Initially, step counts increased by an average of 930 (95% CI 547-1312; P<.001) steps per week, culminating in an average daily step count of 7658 (95% CI 6257-9059; P<.001) at the end of week 3. During the remaining 9 weeks of the study, weekly step counts increased by an average of 67 (95% CI −30 to 163; P<.001) steps per week, resulting in a final estimate of 8258 (95% CI 6933-9584; P<.001) steps. CONCLUSIONS: Participants showed a marked improvement in daily step counts during the first 3 weeks of the study, followed by more gradual improvement in the remaining 9 weeks. Physical activity data and step count recovery trajectories may be considered surrogates for physiological recovery, although further research is needed to examine this relationship. TRIAL REGISTRATION: ClinicalTrials.gov NCT04663945; https://tinyurl.com/2p969ced
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spelling pubmed-101316972023-04-27 Using Wearable Technology to Quantify Physical Activity Recovery: Secondary Report From the AFTER (App-Facilitated Tele-Rehabilitation) Program for COVID-19 Survivors Randomized Study Churchill, Laura Morrow, Mary Capin, Jacob J Jolley, Sarah E Hare, Kristine MaWhinney, Samantha Stevens-Lapsley, Jennifer E Erlandson, Kristine M JMIR Rehabil Assist Technol Original Paper BACKGROUND: Knowledge on physical activity recovery after COVID-19 survival is limited. The AFTER (App-Facilitated Tele-Rehabilitation) program for COVID-19 survivors randomized participants, following hospital discharge, to either education and unstructured physical activity or a telerehabilitation program. Step count data were collected as a secondary outcome, and we found no significant differences in total step count trajectories between groups at 6 weeks. Further step count data were not analyzed. OBJECTIVE: The purpose of this analysis was to examine step count trajectories and correlates among all participants (combined into a single group) across the 12-week study period. METHODS: Linear mixed models with random effects were used to model daily steps over the number of study days. Models with 0, 1, and 2 inflection points were considered, and the final model was selected based on the highest log-likelihood value. RESULTS: Participants included 44 adults (41 with available Fitbit [Fitbit LLC] data). Initially, step counts increased by an average of 930 (95% CI 547-1312; P<.001) steps per week, culminating in an average daily step count of 7658 (95% CI 6257-9059; P<.001) at the end of week 3. During the remaining 9 weeks of the study, weekly step counts increased by an average of 67 (95% CI −30 to 163; P<.001) steps per week, resulting in a final estimate of 8258 (95% CI 6933-9584; P<.001) steps. CONCLUSIONS: Participants showed a marked improvement in daily step counts during the first 3 weeks of the study, followed by more gradual improvement in the remaining 9 weeks. Physical activity data and step count recovery trajectories may be considered surrogates for physiological recovery, although further research is needed to examine this relationship. TRIAL REGISTRATION: ClinicalTrials.gov NCT04663945; https://tinyurl.com/2p969ced JMIR Publications 2023-03-20 /pmc/articles/PMC10131697/ /pubmed/36939818 http://dx.doi.org/10.2196/43436 Text en ©Laura Churchill, Mary Morrow, Jacob J Capin, Sarah E Jolley, Kristine Hare, Samantha MaWhinney, Jennifer E Stevens-Lapsley, Kristine M Erlandson. Originally published in JMIR Rehabilitation and Assistive Technology (https://rehab.jmir.org), 20.03.2023. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Rehabilitation and Assistive Technology, is properly cited. The complete bibliographic information, a link to the original publication on https://rehab.jmir.org/, as well as this copyright and license information must be included.
spellingShingle Original Paper
Churchill, Laura
Morrow, Mary
Capin, Jacob J
Jolley, Sarah E
Hare, Kristine
MaWhinney, Samantha
Stevens-Lapsley, Jennifer E
Erlandson, Kristine M
Using Wearable Technology to Quantify Physical Activity Recovery: Secondary Report From the AFTER (App-Facilitated Tele-Rehabilitation) Program for COVID-19 Survivors Randomized Study
title Using Wearable Technology to Quantify Physical Activity Recovery: Secondary Report From the AFTER (App-Facilitated Tele-Rehabilitation) Program for COVID-19 Survivors Randomized Study
title_full Using Wearable Technology to Quantify Physical Activity Recovery: Secondary Report From the AFTER (App-Facilitated Tele-Rehabilitation) Program for COVID-19 Survivors Randomized Study
title_fullStr Using Wearable Technology to Quantify Physical Activity Recovery: Secondary Report From the AFTER (App-Facilitated Tele-Rehabilitation) Program for COVID-19 Survivors Randomized Study
title_full_unstemmed Using Wearable Technology to Quantify Physical Activity Recovery: Secondary Report From the AFTER (App-Facilitated Tele-Rehabilitation) Program for COVID-19 Survivors Randomized Study
title_short Using Wearable Technology to Quantify Physical Activity Recovery: Secondary Report From the AFTER (App-Facilitated Tele-Rehabilitation) Program for COVID-19 Survivors Randomized Study
title_sort using wearable technology to quantify physical activity recovery: secondary report from the after (app-facilitated tele-rehabilitation) program for covid-19 survivors randomized study
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10131697/
https://www.ncbi.nlm.nih.gov/pubmed/36939818
http://dx.doi.org/10.2196/43436
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