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Characterization of Influenza-Like Illness Burden Using Commercial Wearable Sensor Data and Patient-Reported Outcomes: Mixed Methods Cohort Study
BACKGROUND: The burden of influenza-like illness (ILI) is typically estimated via hospitalizations and deaths. However, ILI-associated morbidity that does not require hospitalization remains poorly characterized. OBJECTIVE: The main objective of this study was to characterize ILI burden using commer...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10131710/ https://www.ncbi.nlm.nih.gov/pubmed/36951890 http://dx.doi.org/10.2196/41050 |
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author | Hunter, Victoria Shapiro, Allison Chawla, Devika Drawnel, Faye Ramirez, Ernesto Phillips, Elizabeth Tadesse-Bell, Sara Foschini, Luca Ukachukwu, Vincent |
author_facet | Hunter, Victoria Shapiro, Allison Chawla, Devika Drawnel, Faye Ramirez, Ernesto Phillips, Elizabeth Tadesse-Bell, Sara Foschini, Luca Ukachukwu, Vincent |
author_sort | Hunter, Victoria |
collection | PubMed |
description | BACKGROUND: The burden of influenza-like illness (ILI) is typically estimated via hospitalizations and deaths. However, ILI-associated morbidity that does not require hospitalization remains poorly characterized. OBJECTIVE: The main objective of this study was to characterize ILI burden using commercial wearable sensor data and investigate the extent to which these data correlate with self-reported illness severity and duration. Furthermore, we aimed to determine whether ILI-associated changes in wearable sensor data differed between care-seeking and non–care-seeking populations as well as between those with confirmed influenza infection and those with ILI symptoms only. METHODS: This study comprised participants enrolled in either the FluStudy2020 or the Home Testing of Respiratory Illness (HTRI) study; both studies were similar in design and conducted between December 2019 and October 2020 in the United States. The participants self-reported ILI-related symptoms and health care–seeking behaviors via daily, biweekly, and monthly surveys. Wearable sensor data were recorded for 120 and 150 days for FluStudy2020 and HTRI, respectively. The following features were assessed: total daily steps, active time (time spent with >50 steps per minute), sleep duration, sleep efficiency, and resting heart rate. ILI-related changes in wearable sensor data were compared between the participants who sought health care and those who did not and between the participants who tested positive for influenza and those with symptoms only. Correlative analyses were performed between wearable sensor data and patient-reported outcomes. RESULTS: After combining the FluStudy2020 and HTRI data sets, the final ILI population comprised 2435 participants. Compared with healthy days (baseline), the participants with ILI exhibited significantly reduced total daily steps, active time, and sleep efficiency as well as increased sleep duration and resting heart rate. Deviations from baseline typically began before symptom onset and were greater in the participants who sought health care than in those who did not and greater in the participants who tested positive for influenza than in those with symptoms only. During an ILI event, changes in wearable sensor data consistently varied with those in patient-reported outcomes. CONCLUSIONS: Our results underscore the potential of wearable sensors to discriminate not only between individuals with and without influenza infections but also between care-seeking and non–care-seeking populations, which may have future application in health care resource planning. TRIAL REGISTRATION: Clinicaltrials.gov NCT04245800; https://clinicaltrials.gov/ct2/show/NCT04245800 |
format | Online Article Text |
id | pubmed-10131710 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | JMIR Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-101317102023-04-27 Characterization of Influenza-Like Illness Burden Using Commercial Wearable Sensor Data and Patient-Reported Outcomes: Mixed Methods Cohort Study Hunter, Victoria Shapiro, Allison Chawla, Devika Drawnel, Faye Ramirez, Ernesto Phillips, Elizabeth Tadesse-Bell, Sara Foschini, Luca Ukachukwu, Vincent J Med Internet Res Original Paper BACKGROUND: The burden of influenza-like illness (ILI) is typically estimated via hospitalizations and deaths. However, ILI-associated morbidity that does not require hospitalization remains poorly characterized. OBJECTIVE: The main objective of this study was to characterize ILI burden using commercial wearable sensor data and investigate the extent to which these data correlate with self-reported illness severity and duration. Furthermore, we aimed to determine whether ILI-associated changes in wearable sensor data differed between care-seeking and non–care-seeking populations as well as between those with confirmed influenza infection and those with ILI symptoms only. METHODS: This study comprised participants enrolled in either the FluStudy2020 or the Home Testing of Respiratory Illness (HTRI) study; both studies were similar in design and conducted between December 2019 and October 2020 in the United States. The participants self-reported ILI-related symptoms and health care–seeking behaviors via daily, biweekly, and monthly surveys. Wearable sensor data were recorded for 120 and 150 days for FluStudy2020 and HTRI, respectively. The following features were assessed: total daily steps, active time (time spent with >50 steps per minute), sleep duration, sleep efficiency, and resting heart rate. ILI-related changes in wearable sensor data were compared between the participants who sought health care and those who did not and between the participants who tested positive for influenza and those with symptoms only. Correlative analyses were performed between wearable sensor data and patient-reported outcomes. RESULTS: After combining the FluStudy2020 and HTRI data sets, the final ILI population comprised 2435 participants. Compared with healthy days (baseline), the participants with ILI exhibited significantly reduced total daily steps, active time, and sleep efficiency as well as increased sleep duration and resting heart rate. Deviations from baseline typically began before symptom onset and were greater in the participants who sought health care than in those who did not and greater in the participants who tested positive for influenza than in those with symptoms only. During an ILI event, changes in wearable sensor data consistently varied with those in patient-reported outcomes. CONCLUSIONS: Our results underscore the potential of wearable sensors to discriminate not only between individuals with and without influenza infections but also between care-seeking and non–care-seeking populations, which may have future application in health care resource planning. TRIAL REGISTRATION: Clinicaltrials.gov NCT04245800; https://clinicaltrials.gov/ct2/show/NCT04245800 JMIR Publications 2023-03-23 /pmc/articles/PMC10131710/ /pubmed/36951890 http://dx.doi.org/10.2196/41050 Text en ©Victoria Hunter, Allison Shapiro, Devika Chawla, Faye Drawnel, Ernesto Ramirez, Elizabeth Phillips, Sara Tadesse-Bell, Luca Foschini, Vincent Ukachukwu. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 23.03.2023. 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 the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included. |
spellingShingle | Original Paper Hunter, Victoria Shapiro, Allison Chawla, Devika Drawnel, Faye Ramirez, Ernesto Phillips, Elizabeth Tadesse-Bell, Sara Foschini, Luca Ukachukwu, Vincent Characterization of Influenza-Like Illness Burden Using Commercial Wearable Sensor Data and Patient-Reported Outcomes: Mixed Methods Cohort Study |
title | Characterization of Influenza-Like Illness Burden Using Commercial Wearable Sensor Data and Patient-Reported Outcomes: Mixed Methods Cohort Study |
title_full | Characterization of Influenza-Like Illness Burden Using Commercial Wearable Sensor Data and Patient-Reported Outcomes: Mixed Methods Cohort Study |
title_fullStr | Characterization of Influenza-Like Illness Burden Using Commercial Wearable Sensor Data and Patient-Reported Outcomes: Mixed Methods Cohort Study |
title_full_unstemmed | Characterization of Influenza-Like Illness Burden Using Commercial Wearable Sensor Data and Patient-Reported Outcomes: Mixed Methods Cohort Study |
title_short | Characterization of Influenza-Like Illness Burden Using Commercial Wearable Sensor Data and Patient-Reported Outcomes: Mixed Methods Cohort Study |
title_sort | characterization of influenza-like illness burden using commercial wearable sensor data and patient-reported outcomes: mixed methods cohort study |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10131710/ https://www.ncbi.nlm.nih.gov/pubmed/36951890 http://dx.doi.org/10.2196/41050 |
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