Comparing Real-Time Self-Tracking and Device-Recorded Exercise Data in Subjects with Type 1 Diabetes

Abstract Background  Insulin therapy, medical nutrition therapy, and physical activity are required for the treatment of type 1 diabetes (T1D). There is a lack of studies in real-life environments that characterize patient-reported data from logs, activity trackers, and medical devices (e.g., glucos...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Applied clinical informatics 2018-10, Vol.9 (4), p.919-926
Hauptverfasser: Groat, Danielle, Kwon, Hyo Jung, Grando, Maria Adela, Cook, Curtiss B., Thompson, Bithika
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 926
container_issue 4
container_start_page 919
container_title Applied clinical informatics
container_volume 9
creator Groat, Danielle
Kwon, Hyo Jung
Grando, Maria Adela
Cook, Curtiss B.
Thompson, Bithika
description Abstract Background  Insulin therapy, medical nutrition therapy, and physical activity are required for the treatment of type 1 diabetes (T1D). There is a lack of studies in real-life environments that characterize patient-reported data from logs, activity trackers, and medical devices (e.g., glucose sensors) in the context of exercise. Objective  The objective of this study was to compare data from continuous glucose monitor (CGM), wristband heart rate monitor (WHRM), and self-tracking with a smartphone application (app), iDECIDE, with regards to exercise behaviors and rate of change in glucose levels. Methods  Participants with T1D on insulin pump therapy tracked exercise for 1 month with the smartphone app while WHRM and CGM recorded data in real time. Exercise behaviors tracked with the app were compared against WHRM. The rate of change in glucose levels, as recorded by CGM, resulting from exercise was compared between exercise events documented with the app and recorded by the WHRM. Results  Twelve participants generated 277 exercise events. Tracking with the app aligned well with WHRM with respect to frequency, 3.0 (2.1) and 2.5 (1.8) days per week, respectively ( p  = 0.60). Duration had very high agreement, the mean duration from the app was 65.6 (55.2) and 64.8 (54.9) minutes from WHRM ( p  = 0.45). Intensity had a low concordance between the data sources (Cohen's kappa = 0.2). The mean rate of change of glucose during exercise was –0.27 mg/(dL*min) and was not significantly different between data sources or intensity ( p  = 0.21). Conclusion  We collated and analyzed data from three heterogeneous sources from free-living participants. Patients' perceived intensity of exercise can serve as a surrogate for exercise tracked by a WHRM when considering the glycemic impact of exercise on self-care regimens.
doi_str_mv 10.1055/s-0038-1676458
format Article
fullrecord <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_6306279</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2161065797</sourcerecordid><originalsourceid>FETCH-LOGICAL-c428t-dc37755ef0952067e8a38b9fc640e62b1834ec1792139d9ee3e31a1bc3b2d4873</originalsourceid><addsrcrecordid>eNp1UclOwzAQtRAIEHDliHzkYrDjeMkFCbVlkZCQ2nK2HGdCXbIUO2H5e1K1IDgwlxnNvHlvNA-hU0YvGBXiMhJKuSZMKpkKvYMOmZYZoTxRu7_qA3QS45IOISTTWu2jA06FllLxQ1SM2nplg2-e8RRsRea-BjyDqiTzYN3Lum-bAo_hzTsgU3BtKKDAkw8IzkfAY9tZ7Bs86_MluC7id98t8PxzBZjhsbc5dBCP0V5pqwgn23yEnm4m89EdeXi8vR9dPxCXJrojheNKCQElzURCpQJtuc6z0smUgkxypnkKjqksYTwrMgAOnFmWO54nRaoVP0JXG95Vn9dQOGi6YCuzCr624dO01pu_k8YvzHP7ZiSnMlHZQHC-JQjtaw-xM7WPDqrKNtD20SRMMiqFytZaFxuoC22MAcofGUbN2h0Tzdods3VnWDj7fdwP_NuLAUA2gG7hoQazbPvQDO_6j_ALuciYYA</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2161065797</pqid></control><display><type>article</type><title>Comparing Real-Time Self-Tracking and Device-Recorded Exercise Data in Subjects with Type 1 Diabetes</title><source>MEDLINE</source><source>PubMed Central</source><source>EZB Electronic Journals Library</source><creator>Groat, Danielle ; Kwon, Hyo Jung ; Grando, Maria Adela ; Cook, Curtiss B. ; Thompson, Bithika</creator><creatorcontrib>Groat, Danielle ; Kwon, Hyo Jung ; Grando, Maria Adela ; Cook, Curtiss B. ; Thompson, Bithika</creatorcontrib><description>Abstract Background  Insulin therapy, medical nutrition therapy, and physical activity are required for the treatment of type 1 diabetes (T1D). There is a lack of studies in real-life environments that characterize patient-reported data from logs, activity trackers, and medical devices (e.g., glucose sensors) in the context of exercise. Objective  The objective of this study was to compare data from continuous glucose monitor (CGM), wristband heart rate monitor (WHRM), and self-tracking with a smartphone application (app), iDECIDE, with regards to exercise behaviors and rate of change in glucose levels. Methods  Participants with T1D on insulin pump therapy tracked exercise for 1 month with the smartphone app while WHRM and CGM recorded data in real time. Exercise behaviors tracked with the app were compared against WHRM. The rate of change in glucose levels, as recorded by CGM, resulting from exercise was compared between exercise events documented with the app and recorded by the WHRM. Results  Twelve participants generated 277 exercise events. Tracking with the app aligned well with WHRM with respect to frequency, 3.0 (2.1) and 2.5 (1.8) days per week, respectively ( p  = 0.60). Duration had very high agreement, the mean duration from the app was 65.6 (55.2) and 64.8 (54.9) minutes from WHRM ( p  = 0.45). Intensity had a low concordance between the data sources (Cohen's kappa = 0.2). The mean rate of change of glucose during exercise was –0.27 mg/(dL*min) and was not significantly different between data sources or intensity ( p  = 0.21). Conclusion  We collated and analyzed data from three heterogeneous sources from free-living participants. Patients' perceived intensity of exercise can serve as a surrogate for exercise tracked by a WHRM when considering the glycemic impact of exercise on self-care regimens.</description><identifier>ISSN: 1869-0327</identifier><identifier>EISSN: 1869-0327</identifier><identifier>DOI: 10.1055/s-0038-1676458</identifier><identifier>PMID: 30586673</identifier><language>eng</language><publisher>Stuttgart · New York: Georg Thieme Verlag KG</publisher><subject>Blood Glucose ; Computer Systems ; Diabetes Mellitus, Type 1 - blood ; Diabetes Mellitus, Type 1 - physiopathology ; Exercise ; Female ; Heart Rate ; Humans ; Male ; Middle Aged ; Mobile Applications ; Monitoring, Physiologic - instrumentation ; Research Article ; Smartphone</subject><ispartof>Applied clinical informatics, 2018-10, Vol.9 (4), p.919-926</ispartof><rights>Georg Thieme Verlag KG Stuttgart · New York.</rights><rights>Thieme Medical Publishers</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c428t-dc37755ef0952067e8a38b9fc640e62b1834ec1792139d9ee3e31a1bc3b2d4873</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6306279/pdf/$$EPDF$$P50$$Gpubmedcentral$$H</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6306279/$$EHTML$$P50$$Gpubmedcentral$$H</linktohtml><link.rule.ids>230,314,723,776,780,881,27903,27904,53770,53772</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/30586673$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Groat, Danielle</creatorcontrib><creatorcontrib>Kwon, Hyo Jung</creatorcontrib><creatorcontrib>Grando, Maria Adela</creatorcontrib><creatorcontrib>Cook, Curtiss B.</creatorcontrib><creatorcontrib>Thompson, Bithika</creatorcontrib><title>Comparing Real-Time Self-Tracking and Device-Recorded Exercise Data in Subjects with Type 1 Diabetes</title><title>Applied clinical informatics</title><addtitle>Appl Clin Inform</addtitle><description>Abstract Background  Insulin therapy, medical nutrition therapy, and physical activity are required for the treatment of type 1 diabetes (T1D). There is a lack of studies in real-life environments that characterize patient-reported data from logs, activity trackers, and medical devices (e.g., glucose sensors) in the context of exercise. Objective  The objective of this study was to compare data from continuous glucose monitor (CGM), wristband heart rate monitor (WHRM), and self-tracking with a smartphone application (app), iDECIDE, with regards to exercise behaviors and rate of change in glucose levels. Methods  Participants with T1D on insulin pump therapy tracked exercise for 1 month with the smartphone app while WHRM and CGM recorded data in real time. Exercise behaviors tracked with the app were compared against WHRM. The rate of change in glucose levels, as recorded by CGM, resulting from exercise was compared between exercise events documented with the app and recorded by the WHRM. Results  Twelve participants generated 277 exercise events. Tracking with the app aligned well with WHRM with respect to frequency, 3.0 (2.1) and 2.5 (1.8) days per week, respectively ( p  = 0.60). Duration had very high agreement, the mean duration from the app was 65.6 (55.2) and 64.8 (54.9) minutes from WHRM ( p  = 0.45). Intensity had a low concordance between the data sources (Cohen's kappa = 0.2). The mean rate of change of glucose during exercise was –0.27 mg/(dL*min) and was not significantly different between data sources or intensity ( p  = 0.21). Conclusion  We collated and analyzed data from three heterogeneous sources from free-living participants. Patients' perceived intensity of exercise can serve as a surrogate for exercise tracked by a WHRM when considering the glycemic impact of exercise on self-care regimens.</description><subject>Blood Glucose</subject><subject>Computer Systems</subject><subject>Diabetes Mellitus, Type 1 - blood</subject><subject>Diabetes Mellitus, Type 1 - physiopathology</subject><subject>Exercise</subject><subject>Female</subject><subject>Heart Rate</subject><subject>Humans</subject><subject>Male</subject><subject>Middle Aged</subject><subject>Mobile Applications</subject><subject>Monitoring, Physiologic - instrumentation</subject><subject>Research Article</subject><subject>Smartphone</subject><issn>1869-0327</issn><issn>1869-0327</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp1UclOwzAQtRAIEHDliHzkYrDjeMkFCbVlkZCQ2nK2HGdCXbIUO2H5e1K1IDgwlxnNvHlvNA-hU0YvGBXiMhJKuSZMKpkKvYMOmZYZoTxRu7_qA3QS45IOISTTWu2jA06FllLxQ1SM2nplg2-e8RRsRea-BjyDqiTzYN3Lum-bAo_hzTsgU3BtKKDAkw8IzkfAY9tZ7Bs86_MluC7id98t8PxzBZjhsbc5dBCP0V5pqwgn23yEnm4m89EdeXi8vR9dPxCXJrojheNKCQElzURCpQJtuc6z0smUgkxypnkKjqksYTwrMgAOnFmWO54nRaoVP0JXG95Vn9dQOGi6YCuzCr624dO01pu_k8YvzHP7ZiSnMlHZQHC-JQjtaw-xM7WPDqrKNtD20SRMMiqFytZaFxuoC22MAcofGUbN2h0Tzdods3VnWDj7fdwP_NuLAUA2gG7hoQazbPvQDO_6j_ALuciYYA</recordid><startdate>20181001</startdate><enddate>20181001</enddate><creator>Groat, Danielle</creator><creator>Kwon, Hyo Jung</creator><creator>Grando, Maria Adela</creator><creator>Cook, Curtiss B.</creator><creator>Thompson, Bithika</creator><general>Georg Thieme Verlag KG</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20181001</creationdate><title>Comparing Real-Time Self-Tracking and Device-Recorded Exercise Data in Subjects with Type 1 Diabetes</title><author>Groat, Danielle ; Kwon, Hyo Jung ; Grando, Maria Adela ; Cook, Curtiss B. ; Thompson, Bithika</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c428t-dc37755ef0952067e8a38b9fc640e62b1834ec1792139d9ee3e31a1bc3b2d4873</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Blood Glucose</topic><topic>Computer Systems</topic><topic>Diabetes Mellitus, Type 1 - blood</topic><topic>Diabetes Mellitus, Type 1 - physiopathology</topic><topic>Exercise</topic><topic>Female</topic><topic>Heart Rate</topic><topic>Humans</topic><topic>Male</topic><topic>Middle Aged</topic><topic>Mobile Applications</topic><topic>Monitoring, Physiologic - instrumentation</topic><topic>Research Article</topic><topic>Smartphone</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Groat, Danielle</creatorcontrib><creatorcontrib>Kwon, Hyo Jung</creatorcontrib><creatorcontrib>Grando, Maria Adela</creatorcontrib><creatorcontrib>Cook, Curtiss B.</creatorcontrib><creatorcontrib>Thompson, Bithika</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Applied clinical informatics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Groat, Danielle</au><au>Kwon, Hyo Jung</au><au>Grando, Maria Adela</au><au>Cook, Curtiss B.</au><au>Thompson, Bithika</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Comparing Real-Time Self-Tracking and Device-Recorded Exercise Data in Subjects with Type 1 Diabetes</atitle><jtitle>Applied clinical informatics</jtitle><addtitle>Appl Clin Inform</addtitle><date>2018-10-01</date><risdate>2018</risdate><volume>9</volume><issue>4</issue><spage>919</spage><epage>926</epage><pages>919-926</pages><issn>1869-0327</issn><eissn>1869-0327</eissn><abstract>Abstract Background  Insulin therapy, medical nutrition therapy, and physical activity are required for the treatment of type 1 diabetes (T1D). There is a lack of studies in real-life environments that characterize patient-reported data from logs, activity trackers, and medical devices (e.g., glucose sensors) in the context of exercise. Objective  The objective of this study was to compare data from continuous glucose monitor (CGM), wristband heart rate monitor (WHRM), and self-tracking with a smartphone application (app), iDECIDE, with regards to exercise behaviors and rate of change in glucose levels. Methods  Participants with T1D on insulin pump therapy tracked exercise for 1 month with the smartphone app while WHRM and CGM recorded data in real time. Exercise behaviors tracked with the app were compared against WHRM. The rate of change in glucose levels, as recorded by CGM, resulting from exercise was compared between exercise events documented with the app and recorded by the WHRM. Results  Twelve participants generated 277 exercise events. Tracking with the app aligned well with WHRM with respect to frequency, 3.0 (2.1) and 2.5 (1.8) days per week, respectively ( p  = 0.60). Duration had very high agreement, the mean duration from the app was 65.6 (55.2) and 64.8 (54.9) minutes from WHRM ( p  = 0.45). Intensity had a low concordance between the data sources (Cohen's kappa = 0.2). The mean rate of change of glucose during exercise was –0.27 mg/(dL*min) and was not significantly different between data sources or intensity ( p  = 0.21). Conclusion  We collated and analyzed data from three heterogeneous sources from free-living participants. Patients' perceived intensity of exercise can serve as a surrogate for exercise tracked by a WHRM when considering the glycemic impact of exercise on self-care regimens.</abstract><cop>Stuttgart · New York</cop><pub>Georg Thieme Verlag KG</pub><pmid>30586673</pmid><doi>10.1055/s-0038-1676458</doi><tpages>8</tpages><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 1869-0327
ispartof Applied clinical informatics, 2018-10, Vol.9 (4), p.919-926
issn 1869-0327
1869-0327
language eng
recordid cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_6306279
source MEDLINE; PubMed Central; EZB Electronic Journals Library
subjects Blood Glucose
Computer Systems
Diabetes Mellitus, Type 1 - blood
Diabetes Mellitus, Type 1 - physiopathology
Exercise
Female
Heart Rate
Humans
Male
Middle Aged
Mobile Applications
Monitoring, Physiologic - instrumentation
Research Article
Smartphone
title Comparing Real-Time Self-Tracking and Device-Recorded Exercise Data in Subjects with Type 1 Diabetes
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-22T16%3A54%3A04IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Comparing%20Real-Time%20Self-Tracking%20and%20Device-Recorded%20Exercise%20Data%20in%20Subjects%20with%20Type%201%20Diabetes&rft.jtitle=Applied%20clinical%20informatics&rft.au=Groat,%20Danielle&rft.date=2018-10-01&rft.volume=9&rft.issue=4&rft.spage=919&rft.epage=926&rft.pages=919-926&rft.issn=1869-0327&rft.eissn=1869-0327&rft_id=info:doi/10.1055/s-0038-1676458&rft_dat=%3Cproquest_pubme%3E2161065797%3C/proquest_pubme%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2161065797&rft_id=info:pmid/30586673&rfr_iscdi=true