Abstract P3092: Improvements in Hemoglobin A1c among Diabetic and Prediabetic Participants of a Mobile Health Cardiovascular Program

Background: Cardiovascular disease (CVD) is the leading cause of death globally, and diabetes is a significant independent risk factor. Mobile health (mHealth) technology can enable effective self-management of CVD risk factors. This study evaluates the impact of an mHealth cardiovascular risk self-management program designed to promote behavioral and lifestyle changes on hemoglobin A1c (HbA1c) among participants with diabetes or prediabetes. Methods: We analyzed real-world data from participants of the mHealth program between January 2020 and April 2024. Participants were included in the analysis if they had an A1c of > 5.7% or a previous diagnosis of diabetes, as well as at least two A1c values available from linked electronic health records. Change in A1c was evaluated from baseline (measured up to 6 months before program enrollment) to follow-up. Participants were stratified according to their baseline A1c values (well-controlled: <7%; fairly-controlled: 7% to 9%; poorly-controlled: >9%) to separately evaluate the effects of program membership on A1c outcomes among individuals with different levels of diabetes severity. Results: A total of 4,358 participants (mean age 56.2±11.2 years, 56.8% female, mean baseline A1c 6.8±1.4%) were included (Table 1). The mean follow-up period was 312.4 days. Pre- and post-enrollment A1c values are graphed in Figure 1. Participants with poorly-controlled baseline A1c (>9%, n =352) showed a significant reduction of 2.1% (from 10.1±1.4% to 8.0±1.9%; t (351) = -18.0, p < 0.001, d = -0.96). Those with fairly-controlled baseline A1c (7–9%, n =1,073) experienced a modest decrease of 0.2% (from 7.5±0.56% to 7.3±1.11%; t (1,072) = -7.2, p < 0.001, d = -0.22). Participants with well-controlled baseline A1c (<7%, n =2,933) remained well-controlled, with a clinically insignificant increase of 0.1% (from 6.0% to 6.1%; t (2,932) = 4.8, p < 0.001, d = 0.09). Conclusion: Participants of an mHealth cardiovascular risk self-management program with poorly-controlled baseline A1c (>9%) decreased A1c significantly, while those with well-controlled baseline A1c (<7%) maintained A1c in this range. These findings highlight the potential of mHealth technology to support control of diabetes in the context of managing CVD risk.

Authors

Institutions

Publication Details

Journal
Circulation
Published
2025-03-11
DOI
https://doi.org/10.1161/cir.151.suppl_1.p3092
Primary Topic
Cardiovascular Health and Risk Factors
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Abstract P3092: Improvements in Hemoglobin A1c among Diabetic and Prediabetic Participants of a Mobile Health Cardiovascular Program

Yehuda Edo Paz, W. Roberts, Helena Lyson
Circulation
Cardiovascular Health and Risk Factors
article

Abstract P3092: Improvements in Hemoglobin A1c among Diabetic and Prediabetic Participants of a Mobile Health Cardiovascular Program

Yehuda Edo Paz, W. Roberts, Helena Lyson
article en

Abstract

Background: Cardiovascular disease (CVD) is the leading cause of death globally, and diabetes is a significant independent risk factor. Mobile health (mHealth) technology can enable effective self-management of CVD risk factors. This study evaluates the impact of an mHealth cardiovascular risk self-management program designed to promote behavioral and lifestyle changes on hemoglobin A1c (HbA1c) among participants with diabetes or prediabetes. Methods: We analyzed real-world data from participants of the mHealth program between January 2020 and April 2024. Participants were included in the analysis if they had an A1c of > 5.7% or a previous diagnosis of diabetes, as well as at least two A1c values available from linked electronic health records. Change in A1c was evaluated from baseline (measured up to 6 months before program enrollment) to follow-up. Participants were stratified according to their baseline A1c values (well-controlled: <7%; fairly-controlled: 7% to 9%; poorly-controlled: >9%) to separately evaluate the effects of program membership on A1c outcomes among individuals with different levels of diabetes severity. Results: A total of 4,358 participants (mean age 56.2±11.2 years, 56.8% female, mean baseline A1c 6.8±1.4%) were included (Table 1). The mean follow-up period was 312.4 days. Pre- and post-enrollment A1c values are graphed in Figure 1. Participants with poorly-controlled baseline A1c (>9%, n =352) showed a significant reduction of 2.1% (from 10.1±1.4% to 8.0±1.9%; t (351) = -18.0, p < 0.001, d = -0.96). Those with fairly-controlled baseline A1c (7–9%, n =1,073) experienced a modest decrease of 0.2% (from 7.5±0.56% to 7.3±1.11%; t (1,072) = -7.2, p < 0.001, d = -0.22). Participants with well-controlled baseline A1c (<7%, n =2,933) remained well-controlled, with a clinically insignificant increase of 0.1% (from 6.0% to 6.1%; t (2,932) = 4.8, p < 0.001, d = 0.09). Conclusion: Participants of an mHealth cardiovascular risk self-management program with poorly-controlled baseline A1c (>9%) decreased A1c significantly, while those with well-controlled baseline A1c (<7%) maintained A1c in this range. These findings highlight the potential of mHealth technology to support control of diabetes in the context of managing CVD risk.

CirculationVol. 151(Suppl_1)
Menlo School (US)
Openalex Percentile: Top 87%
Cardiovascular Health and Risk Factors
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.