Identifying Mobile Health Technology Experiences and Preferences of Low-Income Pregnant Women with Diabetes

Leziak, Karolina; Birch, Eleanor M.; Jackson, Joan L.; Strohbach, Angelina; Niznik, Charlotte M.; Yee, Lynn M.

doi:10.1177/1932296821993175

Cited by 20 publications

(25 citation statements)

References 42 publications

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“…35 Further efforts are needed to address glycemic control including improved access to preconception diabetes care, access to diabetes self-management education, and patient adherence to care, as well as testing innovative technologies aimed at glycemic control, including digital health technology, mobile applications, and continuous glucose monitoring. [36][37][38] This study extends what is known about racial and ethnic disparities in glycemic control as measured by HbA1c among adults with diabetes outside of pregnancy. Repeated studies have demonstrated that non-pregnant non-Hispanic Black adults with diabetes have worse glycemic control compared with non-Hispanic White individuals.…”

Section: Discussionsupporting

confidence: 59%

Section: Discussionmentioning

confidence: 99%

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Differences in Hemoglobin A1c during Pregnancy between Non-Hispanic Black versus White Women with Prepregnancy Diabetes

et al. 2022

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Objective:To determine whether change in HbA1c from early to late pregnancy differs between non-Hispanic Black and White women with prepregnancy diabetes. Study design:A retrospective analysis from an integrated prenatal and diabetes care program from 2012-2016. We compared HbA1c as a continuous measure, and secondarily, HbA1c<6.5%, cross-sectionally and longitudinally in early (~10 weeks) and late (~31 weeks) pregnancy. Linear and logistic regression were used, and adjusted for age, body mass index, White diabetes class, pharmacotherapy, diabetes type, gestational age at baseline HbA1c measurement, and baseline hemoglobin. Results:Among 296 non-Hispanic Black (35%) and White pregnant women (65%) with prepregnancy diabetes (39% type 1, 61% type 2), Black women were more likely experience increased social determinants of health as measured by the Social Vulnerability Index (SVI), and were less likely to have type 1 diabetes and have more severe diabetes versus White women (p<0.05). Black women had higher mean HbA1c (7.8 vs. 7.4%; Beta: 0.75;95%CI: 0.30 to 1.19), and were less likely to have a HbA1c<6.5% at 10 weeks compared to White women (24 vs. 35%;AOR:0.45;95%CI: 0.24 to 0.81); but not after adjusting for SVI. At 31 weeks, both groups had similar mean HbA1c (both 6.5%) and were equally as likely to have a HbA1c<6.5% (57 vs. 54%). From early to late pregnancy, Black women had a higher percentage decrease in HbA1c (1.3% vs. 0.9%;Beta=0.63; 95%CI: 0.27 to 0.99), and were equally as likely to have an improvement or stable HbA1C <6.5% from 10 to 31 weeks, with both groups having a similar mean HbA1c (6.5%) at 31 weeks. Conclusion:Despite experiencing greater community-level social determinants of health, Black women with pregestational diabetes had a larger reduction in HbA1c and were able to equally achieve target HbA1c<6.5% by late pregnancy compared to White women as part of an integrated diabetes and prenatal care prog

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Section: Discussionsupporting

confidence: 59%

Section: Discussionmentioning

confidence: 99%

Differences in Hemoglobin A1c during Pregnancy between Non-Hispanic Black versus White Women with Prepregnancy Diabetes

et al. 2022

View full text Add to dashboard Cite

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“…In secondary analyses among prespecified subgroups, we repeated the above analyses on the subset of individuals with available Hb A 1c data from the periconception period, and modeled the outcome defined using this earlier Hb A 1c , which may be more amenable to interventions aimed at addressing social determinants of health earlier in pregnancy. 12,37 Next, we a priori assessed for effect measure modification or interaction between SVI score and diabetes type (type 1 and 2) and insurance type (Medicaid vs private), and conducted stratified analyses when significant (P,.05). Owing to minimal missing data (3%) for covariates because of manual data abstraction (406/418), we did not perform imputation for missing data in the multivariable regression models.…”

Section: Methodsmentioning

confidence: 99%

Association Between Social Vulnerability and Achieving Glycemic Control Among Pregnant Individuals With Pregestational Diabetes

Venkatesh

Germann

Joseph

et al. 2022

Obstetrics &Amp; Gynecology

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To evaluate the association between community-level social vulnerability and achieving glycemic control (defined as hemoglobin A 1c [Hb A 1c ] less than 6.0% or less than 6.5%) among individuals with pregestational diabetes. METHODS:We conducted a retrospective cohort of individuals with pregestational diabetes with singleton gestations from 2012 to 2016 at a tertiary care center. Addresses were geocoded using ArcGIS and then linked at the census tract to the Centers for Disease Control and Prevention's 2018 SVI (Social Vulnerability Index), which incorporates 15 Census variables to produce a composite score and four scores across thematic domains (socioeconomic status, household composition and disability, minority status and language, and housing type and transportation). Scores range from 0 to 1, with higher values indicating greater community-level social vulnerability. The primary outcome was Hb A 1c less than 6.0%, and, secondarily, Hb A 1c less than 6.5%, in the second or third trimesters. Multivariable Poisson regression with robust error variance was used to evaluate the association between SVI score as a continuous measure and target Hb A 1c .RESULTS: Among 418 assessed pregnant individuals (33.0% type 1; 67.0% type 2 diabetes), 41.4% (173/418) achieved Hb A 1c less than 6.0%, and 56.7% (237/418) Hb A 1c less than 6.5% at a mean gestational age of 29.5 weeks (SD 5.78). Pregnant individuals with a higher SVI score were less likely to achieve Hb A 1c less than 6.0% compared with those with a lower SVI score. For each 0.1-unit increase in SVI score, the risk of achieving Hb A 1c less than 6.0% decreased by nearly 50% (adjusted risk ratio [aRR] 0.53; 95% CI 0.36-0.77), and by more than 30% for Hb A 1c less than 6.5% (adjusted odds ratio 0.67; 95% CI 0.51-0.88). With regard to specific SVI domains, those who scored higher on socioeconomic status (aRR 0.50; 95% CI 0.35-0.71) as well as on household composition and disability (aRR 0.55; 95% CI 0.38-0.79) were less likely to achieve Hb A 1c less than 6.0%.CONCLUSION: Pregnant individuals with pregestational diabetes living in an area with higher social vulnerability were less likely to achieve glycemic control, as measured by HgbA1c levels. Interventions are needed to assess whether addressing social determinants of health can improve glycemic control in pregnancy.

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“…In our study, even though we did not have the equal race distribution, we had participants from low income ( n = 6) and limited education populations (no degree or high school degree, n = 11), yet overall feedback toward using mobile and voice interactive apps was positive. Similar to the literature, there was a demand on mobile technology to improve health outcomes ( 39 , 40 ). The responses to the adoption survey and the collected voice interactive notes show the distribution of technology and voice interaction across demographics.…”

Section: Discussionmentioning

confidence: 77%

“Hey Siri, Help Me Take Care of My Child”: A Feasibility Study With Caregivers of Children With Special Healthcare Needs Using Voice Interaction and Automatic Speech Recognition in Remote Care Management

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Oiler

Abbott

et al. 2022

Front. Public Health

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BackgroundAbout 23% of households in the United States have at least one child who has special healthcare needs. As most care activities occur at home, there is often a disconnect and lack of communication between families, home care nurses, and healthcare providers. Digital health technologies may help bridge this gap.ObjectiveWe conducted a pre-post study with a voice-enabled medical note taking (diary) app (SpeakHealth) in a real world setting with caregivers (parents, family members) of children with special healthcare needs (CSHCN) to understand feasibility of voice interaction and automatic speech recognition (ASR) for medical note taking at home.MethodsIn total, 41 parents of CSHCN were recruited. Participants completed a pre-study survey collecting demographic details, technology and care management preferences. Out of 41, 24 participants completed the study, using the app for 2 weeks and completing an exit survey. The app facilitated caregiver note-taking using voice interaction and ASR. An exit survey was conducted to collect feedback on technology adoption and changes in technology preferences in care management. We assessed the feasibility of the app by descriptively analyzing survey responses and user data following the key focus areas of acceptability, demand, implementation and integration, adaptation and expansion. In addition, perceived effectiveness of the app was assessed by comparing perceived changes in mobile app preferences among participants. In addition, the voice data, notes, and transcriptions were descriptively analyzed for understanding the feasibility of the app.ResultsThe majority of the recruited parents were 35–44 years old (22, 53.7%), part of a two-parent household (30, 73.2%), white (37, 90.2%), had more than one child (31, 75.6%), lived in Ohio (37, 90.2%), used mobile health apps, mobile note taking apps or calendar apps (28, 68.3%) and patient portal apps (22, 53.7%) to track symptoms and health events at home. Caregivers had experience with voice technology as well (32, 78%). Among those completed the post-study survey (in Likert Scale 1–5), ~80% of the caregivers agreed or strongly agreed that using the app would enhance their performance in completing tasks (perceived usefulness; mean = 3.4, SD = 0.8), the app is free of effort (perceived ease of use; mean = 3.2, SD = 0.9), and they would use the app in the future (behavioral intention; mean = 3.1, SD = 0.9). In total, 88 voice interactive patient notes were generated with the majority of the voice recordings being less than 20 s in length (66%). Most noted symptoms and conditions, medications, treatment and therapies, and patient behaviors. More than half of the caregivers reported that voice interaction with the app and using transcribed notes positively changed their preference of technology to use and methods for tracking symptoms and health events at home.ConclusionsOur findings suggested that voice interaction and ASR use in mobile apps are feasible and effective in keeping track of symptoms and health events at home. Future work is suggested toward using integrated and intelligent systems with voice interactions with broader populations.

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Identifying Mobile Health Technology Experiences and Preferences of Low-Income Pregnant Women with Diabetes

Cited by 20 publications

References 42 publications

Differences in Hemoglobin A1c during Pregnancy between Non-Hispanic Black versus White Women with Prepregnancy Diabetes

Differences in Hemoglobin A1c during Pregnancy between Non-Hispanic Black versus White Women with Prepregnancy Diabetes

Association Between Social Vulnerability and Achieving Glycemic Control Among Pregnant Individuals With Pregestational Diabetes

“Hey Siri, Help Me Take Care of My Child”: A Feasibility Study With Caregivers of Children With Special Healthcare Needs Using Voice Interaction and Automatic Speech Recognition in Remote Care Management

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