Cargando…
Smartphone Pedometer Sensor Application for Evaluating Disease Activity and Predicting Comorbidities in Patients with Rheumatoid Arthritis: A Validation Study
Smartphone-based pedometer sensor telemedicine applications could be useful for measuring disease activity and predicting the risk of developing comorbidities, such as pulmonary or cardiovascular disease, in patients with rheumatoid arthritis (RA), but the sensors have not been validated in this pat...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9735816/ https://www.ncbi.nlm.nih.gov/pubmed/36502098 http://dx.doi.org/10.3390/s22239396 |
_version_ | 1784846865621057536 |
---|---|
author | Wagner, Stefan R. Gregersen, Rasmus R. Henriksen, Line Hauge, Ellen-Margrethe Keller, Kresten K. |
author_facet | Wagner, Stefan R. Gregersen, Rasmus R. Henriksen, Line Hauge, Ellen-Margrethe Keller, Kresten K. |
author_sort | Wagner, Stefan R. |
collection | PubMed |
description | Smartphone-based pedometer sensor telemedicine applications could be useful for measuring disease activity and predicting the risk of developing comorbidities, such as pulmonary or cardiovascular disease, in patients with rheumatoid arthritis (RA), but the sensors have not been validated in this patient population. The aim of this study was to validate step counting with an activity-tracking application running the inbuilt Android smartphone pedometer virtual sensor in patients with RA. Two Android-based smartphones were tested in a treadmill test-bed setup at six walking speeds and compared to manual step counting as the gold standard. Guided by a facilitator, the participants walked 100 steps at each test speed, from 2.5 km/h to 5 km/h, wearing both devices simultaneously in a stomach pouch. A computer automatically recorded both the manually observed and the sensor step count. The overall difference in device step counts versus the observed was 5.9% mean absolute percentage error. Highest mean error was at the 2.5 km/h speed tests, where the mean error of the two devices was 18.5%. Both speed and cadence were negatively correlated to the absolute percentage error, which indicates that the greater the speed and cadence, the lower the resulting step counting error rate. There was no correlation between clinical parameters and absolute percentage error. In conclusion, the activity-tracking application using the inbuilt Android smartphone pedometer virtual sensor is valid for step counting in patients with RA. However, walking at very low speed and cadence may represent a challenge. |
format | Online Article Text |
id | pubmed-9735816 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-97358162022-12-11 Smartphone Pedometer Sensor Application for Evaluating Disease Activity and Predicting Comorbidities in Patients with Rheumatoid Arthritis: A Validation Study Wagner, Stefan R. Gregersen, Rasmus R. Henriksen, Line Hauge, Ellen-Margrethe Keller, Kresten K. Sensors (Basel) Article Smartphone-based pedometer sensor telemedicine applications could be useful for measuring disease activity and predicting the risk of developing comorbidities, such as pulmonary or cardiovascular disease, in patients with rheumatoid arthritis (RA), but the sensors have not been validated in this patient population. The aim of this study was to validate step counting with an activity-tracking application running the inbuilt Android smartphone pedometer virtual sensor in patients with RA. Two Android-based smartphones were tested in a treadmill test-bed setup at six walking speeds and compared to manual step counting as the gold standard. Guided by a facilitator, the participants walked 100 steps at each test speed, from 2.5 km/h to 5 km/h, wearing both devices simultaneously in a stomach pouch. A computer automatically recorded both the manually observed and the sensor step count. The overall difference in device step counts versus the observed was 5.9% mean absolute percentage error. Highest mean error was at the 2.5 km/h speed tests, where the mean error of the two devices was 18.5%. Both speed and cadence were negatively correlated to the absolute percentage error, which indicates that the greater the speed and cadence, the lower the resulting step counting error rate. There was no correlation between clinical parameters and absolute percentage error. In conclusion, the activity-tracking application using the inbuilt Android smartphone pedometer virtual sensor is valid for step counting in patients with RA. However, walking at very low speed and cadence may represent a challenge. MDPI 2022-12-02 /pmc/articles/PMC9735816/ /pubmed/36502098 http://dx.doi.org/10.3390/s22239396 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Wagner, Stefan R. Gregersen, Rasmus R. Henriksen, Line Hauge, Ellen-Margrethe Keller, Kresten K. Smartphone Pedometer Sensor Application for Evaluating Disease Activity and Predicting Comorbidities in Patients with Rheumatoid Arthritis: A Validation Study |
title | Smartphone Pedometer Sensor Application for Evaluating Disease Activity and Predicting Comorbidities in Patients with Rheumatoid Arthritis: A Validation Study |
title_full | Smartphone Pedometer Sensor Application for Evaluating Disease Activity and Predicting Comorbidities in Patients with Rheumatoid Arthritis: A Validation Study |
title_fullStr | Smartphone Pedometer Sensor Application for Evaluating Disease Activity and Predicting Comorbidities in Patients with Rheumatoid Arthritis: A Validation Study |
title_full_unstemmed | Smartphone Pedometer Sensor Application for Evaluating Disease Activity and Predicting Comorbidities in Patients with Rheumatoid Arthritis: A Validation Study |
title_short | Smartphone Pedometer Sensor Application for Evaluating Disease Activity and Predicting Comorbidities in Patients with Rheumatoid Arthritis: A Validation Study |
title_sort | smartphone pedometer sensor application for evaluating disease activity and predicting comorbidities in patients with rheumatoid arthritis: a validation study |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9735816/ https://www.ncbi.nlm.nih.gov/pubmed/36502098 http://dx.doi.org/10.3390/s22239396 |
work_keys_str_mv | AT wagnerstefanr smartphonepedometersensorapplicationforevaluatingdiseaseactivityandpredictingcomorbiditiesinpatientswithrheumatoidarthritisavalidationstudy AT gregersenrasmusr smartphonepedometersensorapplicationforevaluatingdiseaseactivityandpredictingcomorbiditiesinpatientswithrheumatoidarthritisavalidationstudy AT henriksenline smartphonepedometersensorapplicationforevaluatingdiseaseactivityandpredictingcomorbiditiesinpatientswithrheumatoidarthritisavalidationstudy AT haugeellenmargrethe smartphonepedometersensorapplicationforevaluatingdiseaseactivityandpredictingcomorbiditiesinpatientswithrheumatoidarthritisavalidationstudy AT kellerkrestenk smartphonepedometersensorapplicationforevaluatingdiseaseactivityandpredictingcomorbiditiesinpatientswithrheumatoidarthritisavalidationstudy |