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Digital Tracking of Rheumatoid Arthritis Longitudinally (DIGITAL) Using Biosensor and Patient-Reported Outcome Data: Protocol for a Real-World Study
BACKGROUND: Rheumatoid arthritis (RA) is a condition with symptoms that vary over time. The typical 3- to 6-month interval between physician visits may lead to patients failing to recall or underreporting symptoms experienced during the interim. Wearable digital technology enables the regular passiv...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6788333/ https://www.ncbi.nlm.nih.gov/pubmed/31573949 http://dx.doi.org/10.2196/14665 |
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author | Nowell, William Benjamin Curtis, Jeffrey R Nolot, Sandra K Curtis, David Venkatachalam, Shilpa Owensby, Justin K Poon, Jiat Ling Calvin, Amy B Kannowski, Carol L Faries, Douglas E Gavigan, Kelly Haynes, Virginia S |
author_facet | Nowell, William Benjamin Curtis, Jeffrey R Nolot, Sandra K Curtis, David Venkatachalam, Shilpa Owensby, Justin K Poon, Jiat Ling Calvin, Amy B Kannowski, Carol L Faries, Douglas E Gavigan, Kelly Haynes, Virginia S |
author_sort | Nowell, William Benjamin |
collection | PubMed |
description | BACKGROUND: Rheumatoid arthritis (RA) is a condition with symptoms that vary over time. The typical 3- to 6-month interval between physician visits may lead to patients failing to recall or underreporting symptoms experienced during the interim. Wearable digital technology enables the regular passive collection of patients’ biometric and activity data. If it is shown to be strongly related to data captured by patient-reported outcome (PRO) measures, information collected passively from wearable digital technology could serve as an objective proxy or be complementary to patients’ subjective experience of RA symptoms. OBJECTIVE: The goal of this study is to characterize the extent to which digital measures collected from a consumer-grade smartwatch agree with measures of RA disease activity and other PROs collected via a smartphone app. METHODS: This observational study will last 6 months for each participant. We aim to recruit 250 members of the ArthritisPower registry with an RA diagnosis who will receive a smartwatch to wear for the period of the study. From the ArthritisPower mobile app on their own smartphone device, participants will be prompted to answer daily and weekly electronic PRO (ePRO) measures for the first 3 months. RESULTS: The study was launched in December 2018 and will require up to 18 months to complete. Study results are expected to be published by the end of 2021. CONCLUSIONS: The completion of this study will provide important data regarding the following: (1) the relationship between passively collected digital measures related to activity, heart rate, and sleep collected from a smartwatch with ePROs related to pain, fatigue, physical function, and RA flare entered via smartphone app; (2) determine predictors of adherence with smartwatch and smartphone app technology; and (3) assess the effect of study-specific reminders on adherence with the smartwatch. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/14665 |
format | Online Article Text |
id | pubmed-6788333 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | JMIR Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-67883332019-10-31 Digital Tracking of Rheumatoid Arthritis Longitudinally (DIGITAL) Using Biosensor and Patient-Reported Outcome Data: Protocol for a Real-World Study Nowell, William Benjamin Curtis, Jeffrey R Nolot, Sandra K Curtis, David Venkatachalam, Shilpa Owensby, Justin K Poon, Jiat Ling Calvin, Amy B Kannowski, Carol L Faries, Douglas E Gavigan, Kelly Haynes, Virginia S JMIR Res Protoc Protocol BACKGROUND: Rheumatoid arthritis (RA) is a condition with symptoms that vary over time. The typical 3- to 6-month interval between physician visits may lead to patients failing to recall or underreporting symptoms experienced during the interim. Wearable digital technology enables the regular passive collection of patients’ biometric and activity data. If it is shown to be strongly related to data captured by patient-reported outcome (PRO) measures, information collected passively from wearable digital technology could serve as an objective proxy or be complementary to patients’ subjective experience of RA symptoms. OBJECTIVE: The goal of this study is to characterize the extent to which digital measures collected from a consumer-grade smartwatch agree with measures of RA disease activity and other PROs collected via a smartphone app. METHODS: This observational study will last 6 months for each participant. We aim to recruit 250 members of the ArthritisPower registry with an RA diagnosis who will receive a smartwatch to wear for the period of the study. From the ArthritisPower mobile app on their own smartphone device, participants will be prompted to answer daily and weekly electronic PRO (ePRO) measures for the first 3 months. RESULTS: The study was launched in December 2018 and will require up to 18 months to complete. Study results are expected to be published by the end of 2021. CONCLUSIONS: The completion of this study will provide important data regarding the following: (1) the relationship between passively collected digital measures related to activity, heart rate, and sleep collected from a smartwatch with ePROs related to pain, fatigue, physical function, and RA flare entered via smartphone app; (2) determine predictors of adherence with smartwatch and smartphone app technology; and (3) assess the effect of study-specific reminders on adherence with the smartwatch. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/14665 JMIR Publications 2019-09-26 /pmc/articles/PMC6788333/ /pubmed/31573949 http://dx.doi.org/10.2196/14665 Text en ©William Benjamin Nowell, Jeffrey R Curtis, Sandra K Nolot, David Curtis, Shilpa Venkatachalam, Justin K Owensby, Jiat Ling Poon, Amy B Calvin, Carol L Kannowski, Douglas E Faries, Kelly Gavigan, Virginia S Haynes. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 26.09.2019 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 Research Protocols, is properly cited. The complete bibliographic information, a link to the original publication on http://www.researchprotocols.org, as well as this copyright and license information must be included. |
spellingShingle | Protocol Nowell, William Benjamin Curtis, Jeffrey R Nolot, Sandra K Curtis, David Venkatachalam, Shilpa Owensby, Justin K Poon, Jiat Ling Calvin, Amy B Kannowski, Carol L Faries, Douglas E Gavigan, Kelly Haynes, Virginia S Digital Tracking of Rheumatoid Arthritis Longitudinally (DIGITAL) Using Biosensor and Patient-Reported Outcome Data: Protocol for a Real-World Study |
title | Digital Tracking of Rheumatoid Arthritis Longitudinally (DIGITAL) Using Biosensor and Patient-Reported Outcome Data: Protocol for a Real-World Study |
title_full | Digital Tracking of Rheumatoid Arthritis Longitudinally (DIGITAL) Using Biosensor and Patient-Reported Outcome Data: Protocol for a Real-World Study |
title_fullStr | Digital Tracking of Rheumatoid Arthritis Longitudinally (DIGITAL) Using Biosensor and Patient-Reported Outcome Data: Protocol for a Real-World Study |
title_full_unstemmed | Digital Tracking of Rheumatoid Arthritis Longitudinally (DIGITAL) Using Biosensor and Patient-Reported Outcome Data: Protocol for a Real-World Study |
title_short | Digital Tracking of Rheumatoid Arthritis Longitudinally (DIGITAL) Using Biosensor and Patient-Reported Outcome Data: Protocol for a Real-World Study |
title_sort | digital tracking of rheumatoid arthritis longitudinally (digital) using biosensor and patient-reported outcome data: protocol for a real-world study |
topic | Protocol |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6788333/ https://www.ncbi.nlm.nih.gov/pubmed/31573949 http://dx.doi.org/10.2196/14665 |
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