Alison K Brinson, Hannah R Jahnke, Natalie Henrich, Christa Moss, Neel Shah
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Multivariable linear and logistic regression models were used to estimate the associations between hours of digital health use and (1) gestational age at birth and (2) mechanisms to reduce the risk of a NICU admission. Multivariable logistic regression models estimated the associations between (1) gestational age at birth and (2) mechanisms to reduce the risk of a NICU admission and the likelihood of a NICU admission. All analyses were stratified by the presence of any gestational conditions during pregnancy.</p><p><strong>Results: </strong>For users both with and without gestational conditions, hours of digital health use were positively associated with gestational age at birth (in weeks; with gestational conditions: β=.01; 95% CI 0.0006-0.02; P=.04 and without gestational conditions: β=.01; 95% CI 0.0006-0.02; P=.04) and mechanisms that have the potential to reduce risk of a NICU admission, including learning medically accurate information (with gestational conditions: adjusted odds ratio [AOR] 1.05, 95% CI 1.03-1.07; P<.001 and without gestational conditions: AOR 1.04, 95% CI 1.02-1.06; P<.001), mental health management (with gestational conditions: AOR 1.06, 95% CI 1.04-1.08; P<.001 and without gestational conditions: AOR 1.03, 95% CI 1.02-1.05; P<.001), and understanding warning signs during pregnancy (with gestational conditions: AOR 1.08, 95% CI 1.06-1.11; P<.001 and without gestational conditions: AOR 1.09, 95% CI 1.07-1.11; P<.001). For users with and without gestational conditions, an increase in gestational age at birth was associated with a decreased likelihood of NICU admission (with gestational conditions: AOR 0.62, 95% CI 0.55-0.69; P<.001 and without gestational conditions: AOR 0.59, 95% CI 0.53-0.65; P<.001). Among users who developed gestational conditions, those who reported that the platform helped them understand warning signs during pregnancy had lower odds of a NICU admission (AOR 0.63, 95% CI 0.45-0.89; P=.01).</p><p><strong>Conclusions: </strong>Digital health use may aid in extending gestational age at birth and reduce the risk of NICU admission.</p>","PeriodicalId":36223,"journal":{"name":"JMIR Pediatrics and Parenting","volume":null,"pages":null},"PeriodicalIF":2.1000,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11498062/pdf/","citationCount":"0","resultStr":"{\"title\":\"Digital Health as a Mechanism to Reduce Neonatal Intensive Care Unit Admissions: Retrospective Cohort Study.\",\"authors\":\"Alison K Brinson, Hannah R Jahnke, Natalie Henrich, Christa Moss, Neel Shah\",\"doi\":\"10.2196/56247\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Admission to the neonatal intensive care unit (NICU) is costly and has been associated with financial and emotional stress among families. Digital health may be well equipped to impact modifiable health factors that contribute to NICU admission rates.</p><p><strong>Objective: </strong>The aim of the study is to investigate how the use of a comprehensive prenatal digital health platform is associated with gestational age at birth and mechanisms to reduce the risk of admission to the NICU.</p><p><strong>Methods: </strong>Data were extracted from 3326 users who enrolled in a comprehensive digital health platform between January 2020 and May 2022. Multivariable linear and logistic regression models were used to estimate the associations between hours of digital health use and (1) gestational age at birth and (2) mechanisms to reduce the risk of a NICU admission. Multivariable logistic regression models estimated the associations between (1) gestational age at birth and (2) mechanisms to reduce the risk of a NICU admission and the likelihood of a NICU admission. All analyses were stratified by the presence of any gestational conditions during pregnancy.</p><p><strong>Results: </strong>For users both with and without gestational conditions, hours of digital health use were positively associated with gestational age at birth (in weeks; with gestational conditions: β=.01; 95% CI 0.0006-0.02; P=.04 and without gestational conditions: β=.01; 95% CI 0.0006-0.02; P=.04) and mechanisms that have the potential to reduce risk of a NICU admission, including learning medically accurate information (with gestational conditions: adjusted odds ratio [AOR] 1.05, 95% CI 1.03-1.07; P<.001 and without gestational conditions: AOR 1.04, 95% CI 1.02-1.06; P<.001), mental health management (with gestational conditions: AOR 1.06, 95% CI 1.04-1.08; P<.001 and without gestational conditions: AOR 1.03, 95% CI 1.02-1.05; P<.001), and understanding warning signs during pregnancy (with gestational conditions: AOR 1.08, 95% CI 1.06-1.11; P<.001 and without gestational conditions: AOR 1.09, 95% CI 1.07-1.11; P<.001). For users with and without gestational conditions, an increase in gestational age at birth was associated with a decreased likelihood of NICU admission (with gestational conditions: AOR 0.62, 95% CI 0.55-0.69; P<.001 and without gestational conditions: AOR 0.59, 95% CI 0.53-0.65; P<.001). Among users who developed gestational conditions, those who reported that the platform helped them understand warning signs during pregnancy had lower odds of a NICU admission (AOR 0.63, 95% CI 0.45-0.89; P=.01).</p><p><strong>Conclusions: </strong>Digital health use may aid in extending gestational age at birth and reduce the risk of NICU admission.</p>\",\"PeriodicalId\":36223,\"journal\":{\"name\":\"JMIR Pediatrics and Parenting\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.1000,\"publicationDate\":\"2024-10-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11498062/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"JMIR Pediatrics and Parenting\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2196/56247\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"PEDIATRICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"JMIR Pediatrics and Parenting","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2196/56247","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PEDIATRICS","Score":null,"Total":0}
引用次数: 0
摘要
背景:新生儿重症监护室(NICU)的入院费用昂贵,而且与家庭的经济和精神压力有关。数字医疗可能有能力影响导致新生儿重症监护室入院率的可改变的健康因素:本研究旨在调查产前综合数字健康平台的使用与出生时胎龄的关系,以及降低入住新生儿重症监护室风险的机制:从2020年1月至2022年5月期间注册综合数字健康平台的3326名用户中提取数据。多变量线性回归模型和逻辑回归模型用于估算数字健康使用时长与(1)出生时的胎龄和(2)降低入住新生儿重症监护室风险的机制之间的关系。多变量逻辑回归模型估算了(1)出生时的胎龄和(2)降低新生儿重症监护室入院风险的机制与新生儿重症监护室入院可能性之间的关系。所有分析均按孕期是否存在妊娠期疾病进行分层:结果:对于有妊娠期疾病和无妊娠期疾病的用户,使用数字健康服务的时间与出生时的胎龄呈正相关(以周为单位;有妊娠期疾病:β=.01;95% CI 0.0006-0.02;P=.04;无妊娠期疾病:β=.01;95% CI 0.0006-0.02;P=.05)。01;95% CI 0.0006-0.02;P=.04),以及有可能降低入住新生儿重症监护室风险的机制,包括学习准确的医学信息(有妊娠条件:调整赔率比[AOR]1.05,95% CI 1.03-1.07;PC结论:数字医疗的使用可能有助于延长婴儿出生时的胎龄并降低入住新生儿重症监护室的风险。
Digital Health as a Mechanism to Reduce Neonatal Intensive Care Unit Admissions: Retrospective Cohort Study.
Background: Admission to the neonatal intensive care unit (NICU) is costly and has been associated with financial and emotional stress among families. Digital health may be well equipped to impact modifiable health factors that contribute to NICU admission rates.
Objective: The aim of the study is to investigate how the use of a comprehensive prenatal digital health platform is associated with gestational age at birth and mechanisms to reduce the risk of admission to the NICU.
Methods: Data were extracted from 3326 users who enrolled in a comprehensive digital health platform between January 2020 and May 2022. Multivariable linear and logistic regression models were used to estimate the associations between hours of digital health use and (1) gestational age at birth and (2) mechanisms to reduce the risk of a NICU admission. Multivariable logistic regression models estimated the associations between (1) gestational age at birth and (2) mechanisms to reduce the risk of a NICU admission and the likelihood of a NICU admission. All analyses were stratified by the presence of any gestational conditions during pregnancy.
Results: For users both with and without gestational conditions, hours of digital health use were positively associated with gestational age at birth (in weeks; with gestational conditions: β=.01; 95% CI 0.0006-0.02; P=.04 and without gestational conditions: β=.01; 95% CI 0.0006-0.02; P=.04) and mechanisms that have the potential to reduce risk of a NICU admission, including learning medically accurate information (with gestational conditions: adjusted odds ratio [AOR] 1.05, 95% CI 1.03-1.07; P<.001 and without gestational conditions: AOR 1.04, 95% CI 1.02-1.06; P<.001), mental health management (with gestational conditions: AOR 1.06, 95% CI 1.04-1.08; P<.001 and without gestational conditions: AOR 1.03, 95% CI 1.02-1.05; P<.001), and understanding warning signs during pregnancy (with gestational conditions: AOR 1.08, 95% CI 1.06-1.11; P<.001 and without gestational conditions: AOR 1.09, 95% CI 1.07-1.11; P<.001). For users with and without gestational conditions, an increase in gestational age at birth was associated with a decreased likelihood of NICU admission (with gestational conditions: AOR 0.62, 95% CI 0.55-0.69; P<.001 and without gestational conditions: AOR 0.59, 95% CI 0.53-0.65; P<.001). Among users who developed gestational conditions, those who reported that the platform helped them understand warning signs during pregnancy had lower odds of a NICU admission (AOR 0.63, 95% CI 0.45-0.89; P=.01).
Conclusions: Digital health use may aid in extending gestational age at birth and reduce the risk of NICU admission.