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Analyzing Sensor-Based Time Series Data to Track Changes in Physical Activity during Inpatient Rehabilitation
Time series data collected from sensors can be analyzed to monitor changes in physical activity as an individual makes a substantial lifestyle change, such as recovering from an injury or illness. In an inpatient rehabilitation setting, approaches to detect and explain changes in longitudinal physic...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5677114/ https://www.ncbi.nlm.nih.gov/pubmed/28953257 http://dx.doi.org/10.3390/s17102219 |
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author | Sprint, Gina Cook, Diane Weeks, Douglas Dahmen, Jordana La Fleur, Alyssa |
author_facet | Sprint, Gina Cook, Diane Weeks, Douglas Dahmen, Jordana La Fleur, Alyssa |
author_sort | Sprint, Gina |
collection | PubMed |
description | Time series data collected from sensors can be analyzed to monitor changes in physical activity as an individual makes a substantial lifestyle change, such as recovering from an injury or illness. In an inpatient rehabilitation setting, approaches to detect and explain changes in longitudinal physical activity data collected from wearable sensors can provide value as a monitoring, research, and motivating tool. We adapt and expand our Physical Activity Change Detection (PACD) approach to analyze changes in patient activity in such a setting. We use Fitbit Charge Heart Rate devices with two separate populations to continuously record data to evaluate PACD, nine participants in a hospitalized inpatient rehabilitation group and eight in a healthy control group. We apply PACD to minute-by-minute Fitbit data to quantify changes within and between the groups. The inpatient rehabilitation group exhibited greater variability in change throughout inpatient rehabilitation for both step count and heart rate, with the greatest change occurring at the end of the inpatient hospital stay, which exceeded day-to-day changes of the control group. Our additions to PACD support effective change analysis of wearable sensor data collected in an inpatient rehabilitation setting and provide insight to patients, clinicians, and researchers. |
format | Online Article Text |
id | pubmed-5677114 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-56771142017-11-17 Analyzing Sensor-Based Time Series Data to Track Changes in Physical Activity during Inpatient Rehabilitation Sprint, Gina Cook, Diane Weeks, Douglas Dahmen, Jordana La Fleur, Alyssa Sensors (Basel) Article Time series data collected from sensors can be analyzed to monitor changes in physical activity as an individual makes a substantial lifestyle change, such as recovering from an injury or illness. In an inpatient rehabilitation setting, approaches to detect and explain changes in longitudinal physical activity data collected from wearable sensors can provide value as a monitoring, research, and motivating tool. We adapt and expand our Physical Activity Change Detection (PACD) approach to analyze changes in patient activity in such a setting. We use Fitbit Charge Heart Rate devices with two separate populations to continuously record data to evaluate PACD, nine participants in a hospitalized inpatient rehabilitation group and eight in a healthy control group. We apply PACD to minute-by-minute Fitbit data to quantify changes within and between the groups. The inpatient rehabilitation group exhibited greater variability in change throughout inpatient rehabilitation for both step count and heart rate, with the greatest change occurring at the end of the inpatient hospital stay, which exceeded day-to-day changes of the control group. Our additions to PACD support effective change analysis of wearable sensor data collected in an inpatient rehabilitation setting and provide insight to patients, clinicians, and researchers. MDPI 2017-09-27 /pmc/articles/PMC5677114/ /pubmed/28953257 http://dx.doi.org/10.3390/s17102219 Text en © 2017 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 Sprint, Gina Cook, Diane Weeks, Douglas Dahmen, Jordana La Fleur, Alyssa Analyzing Sensor-Based Time Series Data to Track Changes in Physical Activity during Inpatient Rehabilitation |
title | Analyzing Sensor-Based Time Series Data to Track Changes in Physical Activity during Inpatient Rehabilitation |
title_full | Analyzing Sensor-Based Time Series Data to Track Changes in Physical Activity during Inpatient Rehabilitation |
title_fullStr | Analyzing Sensor-Based Time Series Data to Track Changes in Physical Activity during Inpatient Rehabilitation |
title_full_unstemmed | Analyzing Sensor-Based Time Series Data to Track Changes in Physical Activity during Inpatient Rehabilitation |
title_short | Analyzing Sensor-Based Time Series Data to Track Changes in Physical Activity during Inpatient Rehabilitation |
title_sort | analyzing sensor-based time series data to track changes in physical activity during inpatient rehabilitation |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5677114/ https://www.ncbi.nlm.nih.gov/pubmed/28953257 http://dx.doi.org/10.3390/s17102219 |
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