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...
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Veröffentlicht in: | Applied clinical informatics 2018-10, Vol.9 (4), p.919-926 |
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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 |
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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> |
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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 |
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