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Leveraging Mobile-Based Sensors for Clinical Research to Obtain Activity and Health Measures for Disease Monitoring, Prevention, and Treatment

Clinical researchers are using mobile-based sensors to obtain detailed and objective measures of the activity and health of research participants, but many investigators lack expertise in integrating wearables and sensor technologies effectively into their studies. Here, we describe the steps taken...

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Autores principales: Dandapani, Hari G., Davoodi, Natalie M., Joerg, Lucie C., Li, Melinda M., Strauss, Daniel H., Fan, Kelly, Massachi, Talie, Goldberg, Elizabeth M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9237242/
https://www.ncbi.nlm.nih.gov/pubmed/35774115
http://dx.doi.org/10.3389/fdgth.2022.893070
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author Dandapani, Hari G.
Davoodi, Natalie M.
Joerg, Lucie C.
Li, Melinda M.
Strauss, Daniel H.
Fan, Kelly
Massachi, Talie
Goldberg, Elizabeth M.
author_facet Dandapani, Hari G.
Davoodi, Natalie M.
Joerg, Lucie C.
Li, Melinda M.
Strauss, Daniel H.
Fan, Kelly
Massachi, Talie
Goldberg, Elizabeth M.
author_sort Dandapani, Hari G.
collection PubMed
description Clinical researchers are using mobile-based sensors to obtain detailed and objective measures of the activity and health of research participants, but many investigators lack expertise in integrating wearables and sensor technologies effectively into their studies. Here, we describe the steps taken to design a study using sensors for disease monitoring in older adults and explore the benefits and drawbacks of our approach. In this study, the Geriatric Acute and Post-acute Fall Prevention Intervention (GAPcare), we created an iOS app to collect data from the Apple Watch's gyroscope, accelerometer, and other sensors; results of cognitive and fitness tests; and participant-entered survey data. We created the study app using ResearchKit, an open-source framework developed by Apple for medical research that includes neuropsychological tests (e.g., of executive function and memory), gait speed, balance, and other health assessments. Data is transmitted via an Application Programming Interface (API) from the app to REDCap for researchers to monitor and analyze in real-time. Employing the lessons learned from GAPcare could help researchers create study-tailored research apps and access timely information about their research participants from wearables and smartphone devices for disease prevention, monitoring, and treatment.
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spelling pubmed-92372422022-06-29 Leveraging Mobile-Based Sensors for Clinical Research to Obtain Activity and Health Measures for Disease Monitoring, Prevention, and Treatment Dandapani, Hari G. Davoodi, Natalie M. Joerg, Lucie C. Li, Melinda M. Strauss, Daniel H. Fan, Kelly Massachi, Talie Goldberg, Elizabeth M. Front Digit Health Digital Health Clinical researchers are using mobile-based sensors to obtain detailed and objective measures of the activity and health of research participants, but many investigators lack expertise in integrating wearables and sensor technologies effectively into their studies. Here, we describe the steps taken to design a study using sensors for disease monitoring in older adults and explore the benefits and drawbacks of our approach. In this study, the Geriatric Acute and Post-acute Fall Prevention Intervention (GAPcare), we created an iOS app to collect data from the Apple Watch's gyroscope, accelerometer, and other sensors; results of cognitive and fitness tests; and participant-entered survey data. We created the study app using ResearchKit, an open-source framework developed by Apple for medical research that includes neuropsychological tests (e.g., of executive function and memory), gait speed, balance, and other health assessments. Data is transmitted via an Application Programming Interface (API) from the app to REDCap for researchers to monitor and analyze in real-time. Employing the lessons learned from GAPcare could help researchers create study-tailored research apps and access timely information about their research participants from wearables and smartphone devices for disease prevention, monitoring, and treatment. Frontiers Media S.A. 2022-06-14 /pmc/articles/PMC9237242/ /pubmed/35774115 http://dx.doi.org/10.3389/fdgth.2022.893070 Text en Copyright © 2022 Dandapani, Davoodi, Joerg, Li, Strauss, Fan, Massachi and Goldberg. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Digital Health
Dandapani, Hari G.
Davoodi, Natalie M.
Joerg, Lucie C.
Li, Melinda M.
Strauss, Daniel H.
Fan, Kelly
Massachi, Talie
Goldberg, Elizabeth M.
Leveraging Mobile-Based Sensors for Clinical Research to Obtain Activity and Health Measures for Disease Monitoring, Prevention, and Treatment
title Leveraging Mobile-Based Sensors for Clinical Research to Obtain Activity and Health Measures for Disease Monitoring, Prevention, and Treatment
title_full Leveraging Mobile-Based Sensors for Clinical Research to Obtain Activity and Health Measures for Disease Monitoring, Prevention, and Treatment
title_fullStr Leveraging Mobile-Based Sensors for Clinical Research to Obtain Activity and Health Measures for Disease Monitoring, Prevention, and Treatment
title_full_unstemmed Leveraging Mobile-Based Sensors for Clinical Research to Obtain Activity and Health Measures for Disease Monitoring, Prevention, and Treatment
title_short Leveraging Mobile-Based Sensors for Clinical Research to Obtain Activity and Health Measures for Disease Monitoring, Prevention, and Treatment
title_sort leveraging mobile-based sensors for clinical research to obtain activity and health measures for disease monitoring, prevention, and treatment
topic Digital Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9237242/
https://www.ncbi.nlm.nih.gov/pubmed/35774115
http://dx.doi.org/10.3389/fdgth.2022.893070
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