Evaluating the Influence of Mood and Stress on GlycemicVariability in People with T1DM Using Glucose MonitoringSensors and Pools

Objective: Assess in a sample of people with type 1 diabetes mellitus whether mood andstress influence blood glucose levels and variability.Material and Methods: Continuous glucosemonitoring was performed on 10 patients with type 1 diabetes mellitus, where interstitial glucosevalues were recorded ev...

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Autores: Velasco, Jose Manuel, Botella-Serrano, Marta, Sánchez Sánchez, Almudena, Aramendi, Aranzazu, Martínez, Remedios, Maqueda, Esther, Garnica, Óscar, Contador, Sergio, Lanchares, Juan, Hidalgo, J. Ignacio
Tipo de recurso: artículo
Fecha de publicación:2022
País:España
Institución:Universidad a Distancia de Madrid (UDIMA)
Repositorio:udiMundus. Repositorio Institucional de la Universidad a Distancia de Madrid
OAI Identifier:oai:udimundus.udima.es:20.500.12226/1397
Acceso en línea:http://hdl.handle.net/20.500.12226/1397
Access Level:acceso abierto
Palabra clave:stress
glucose variability
mood
Continuous Glucose Monitoring
glycemic control
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spelling Evaluating the Influence of Mood and Stress on GlycemicVariability in People with T1DM Using Glucose MonitoringSensors and PoolsVelasco, Jose ManuelBotella-Serrano, MartaSánchez Sánchez, AlmudenaAramendi, AranzazuMartínez, RemediosMaqueda, EstherGarnica, ÓscarContador, SergioLanchares, JuanHidalgo, J. Ignaciostressglucose variabilitymoodContinuous Glucose Monitoringglycemic controlObjective: Assess in a sample of people with type 1 diabetes mellitus whether mood andstress influence blood glucose levels and variability.Material and Methods: Continuous glucosemonitoring was performed on 10 patients with type 1 diabetes mellitus, where interstitial glucosevalues were recorded every 15 min. A daily survey was conducted through Google Forms, collectinginformation on mood and stress. The day was divided into six slots of 4-h each, asking the patientto assess each slot in relation to mood (sad, normal or happy) and stress (calm, normal or nervous).Different measures of glycemic control (arithmetic mean and percentage of time below/above thetarget range) and variability (standard deviation, percentage coefficient of variation, mean amplitudeof glycemic excursions and mean of daily differences) were calculated to relate the mood and stressperceived by patients with blood glucose levels and glycemic variability. A hypothesis test wascarried out to quantitatively compare the data groups of the different measures using the Student’st-test.Results: Statistically significant differences (p-value < 0.05) were found between differentlevels of stress. In general, average glucose and variability decrease when the patient is calm. Thereare statistically significant differences (p-value < 0.05) between different levels of mood. Variabilityincreases when the mood changes from sad to happy. However, the patient’s average glucosedecreases as the mood improves.Conclusions: Variations in mood and stress significantly influenceblood glucose levels, and glycemic variability in the patients analyzed with type 1 diabetes mellitus.Therefore, they are factors to consider for improving glycemic control. The mean of daily differencesdoes not seem to be a good indicator for variability.2021-22Instituto de Investigación, Desarrollo e InnovaciónFacultad de Ciencias de la Salud y de la Educación2022info:eu-repo/semantics/articlehttp://hdl.handle.net/20.500.12226/1397reponame:udiMundus. Repositorio Institucional de la Universidad a Distancia de Madridinstname:Universidad a Distancia de Madrid (UDIMA)InglésThis research was funded by Fundación Eugenio Rodríguez Pascual 2019–2020, GLENOProject. Ministerio de Economía y Competitividad under grant TIN2014-54806-R. Ministerio de Cien-cia, Innovación y Universidades under grant RTI2018-095180-B-I00. Comunidad de Madrid undergrants B2017/BMD3773 (GenObIA-CM) and Y2018/NMT-4668 (Micro-Stress-MAP-CM). EuropeanUnion through structural and FEDER Funds.info:eu-repo/semantics/openAccessoai:udimundus.udima.es:20.500.12226/13972026-06-02T12:44:31Z
dc.title.none.fl_str_mv Evaluating the Influence of Mood and Stress on GlycemicVariability in People with T1DM Using Glucose MonitoringSensors and Pools
title Evaluating the Influence of Mood and Stress on GlycemicVariability in People with T1DM Using Glucose MonitoringSensors and Pools
spellingShingle Evaluating the Influence of Mood and Stress on GlycemicVariability in People with T1DM Using Glucose MonitoringSensors and Pools
Velasco, Jose Manuel
stress
glucose variability
mood
Continuous Glucose Monitoring
glycemic control
title_short Evaluating the Influence of Mood and Stress on GlycemicVariability in People with T1DM Using Glucose MonitoringSensors and Pools
title_full Evaluating the Influence of Mood and Stress on GlycemicVariability in People with T1DM Using Glucose MonitoringSensors and Pools
title_fullStr Evaluating the Influence of Mood and Stress on GlycemicVariability in People with T1DM Using Glucose MonitoringSensors and Pools
title_full_unstemmed Evaluating the Influence of Mood and Stress on GlycemicVariability in People with T1DM Using Glucose MonitoringSensors and Pools
title_sort Evaluating the Influence of Mood and Stress on GlycemicVariability in People with T1DM Using Glucose MonitoringSensors and Pools
dc.creator.none.fl_str_mv Velasco, Jose Manuel
Botella-Serrano, Marta
Sánchez Sánchez, Almudena
Aramendi, Aranzazu
Martínez, Remedios
Maqueda, Esther
Garnica, Óscar
Contador, Sergio
Lanchares, Juan
Hidalgo, J. Ignacio
author Velasco, Jose Manuel
author_facet Velasco, Jose Manuel
Botella-Serrano, Marta
Sánchez Sánchez, Almudena
Aramendi, Aranzazu
Martínez, Remedios
Maqueda, Esther
Garnica, Óscar
Contador, Sergio
Lanchares, Juan
Hidalgo, J. Ignacio
author_role author
author2 Botella-Serrano, Marta
Sánchez Sánchez, Almudena
Aramendi, Aranzazu
Martínez, Remedios
Maqueda, Esther
Garnica, Óscar
Contador, Sergio
Lanchares, Juan
Hidalgo, J. Ignacio
author2_role author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv stress
glucose variability
mood
Continuous Glucose Monitoring
glycemic control
topic stress
glucose variability
mood
Continuous Glucose Monitoring
glycemic control
description Objective: Assess in a sample of people with type 1 diabetes mellitus whether mood andstress influence blood glucose levels and variability.Material and Methods: Continuous glucosemonitoring was performed on 10 patients with type 1 diabetes mellitus, where interstitial glucosevalues were recorded every 15 min. A daily survey was conducted through Google Forms, collectinginformation on mood and stress. The day was divided into six slots of 4-h each, asking the patientto assess each slot in relation to mood (sad, normal or happy) and stress (calm, normal or nervous).Different measures of glycemic control (arithmetic mean and percentage of time below/above thetarget range) and variability (standard deviation, percentage coefficient of variation, mean amplitudeof glycemic excursions and mean of daily differences) were calculated to relate the mood and stressperceived by patients with blood glucose levels and glycemic variability. A hypothesis test wascarried out to quantitatively compare the data groups of the different measures using the Student’st-test.Results: Statistically significant differences (p-value < 0.05) were found between differentlevels of stress. In general, average glucose and variability decrease when the patient is calm. Thereare statistically significant differences (p-value < 0.05) between different levels of mood. Variabilityincreases when the mood changes from sad to happy. However, the patient’s average glucosedecreases as the mood improves.Conclusions: Variations in mood and stress significantly influenceblood glucose levels, and glycemic variability in the patients analyzed with type 1 diabetes mellitus.Therefore, they are factors to consider for improving glycemic control. The mean of daily differencesdoes not seem to be a good indicator for variability.
publishDate 2022
dc.date.none.fl_str_mv 2022
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/20.500.12226/1397
url http://hdl.handle.net/20.500.12226/1397
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv This research was funded by Fundación Eugenio Rodríguez Pascual 2019–2020, GLENOProject. Ministerio de Economía y Competitividad under grant TIN2014-54806-R. Ministerio de Cien-cia, Innovación y Universidades under grant RTI2018-095180-B-I00. Comunidad de Madrid undergrants B2017/BMD3773 (GenObIA-CM) and Y2018/NMT-4668 (Micro-Stress-MAP-CM). EuropeanUnion through structural and FEDER Funds.
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Instituto de Investigación, Desarrollo e Innovación
Facultad de Ciencias de la Salud y de la Educación
publisher.none.fl_str_mv Instituto de Investigación, Desarrollo e Innovación
Facultad de Ciencias de la Salud y de la Educación
dc.source.none.fl_str_mv reponame:udiMundus. Repositorio Institucional de la Universidad a Distancia de Madrid
instname:Universidad a Distancia de Madrid (UDIMA)
instname_str Universidad a Distancia de Madrid (UDIMA)
reponame_str udiMundus. Repositorio Institucional de la Universidad a Distancia de Madrid
collection udiMundus. Repositorio Institucional de la Universidad a Distancia de Madrid
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,301603

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