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探索针对有妊娠期糖尿病风险的女性的移动健康干预中的参与模式。

Exploring engagement patterns within a mobile health intervention for women at risk of gestational diabetes.

作者信息

Bendsen Signe B, Skinner Timothy C, O'Reilly Sharleen L, Rey Velasco Elena, Heltberg Mathias S, Laursen Ditte H

机构信息

Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark.

Department of Psychology, Faculty of Social Sciences, University of Copenhagen, Copenhagen, Denmark.

出版信息

Womens Health (Lond). 2025 Jan-Dec;21:17455057251327510. doi: 10.1177/17455057251327510. Epub 2025 Jun 5.

DOI:10.1177/17455057251327510
PMID:40470610
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12141804/
Abstract

BACKGROUND: Gestational diabetes mellitus poses a significant global health concern during pregnancy, with behaviour change interventions offering effective risk reduction. OBJECTIVES: Understanding diverse engagement patterns of pregnant women within mobile health (mHealth) interventions is vital for personalised healthcare. Tailoring interventions based on participant engagement types can enhance program effectiveness. This study aimed to explore engagement patterns among pregnant women at risk of gestational diabetes using the Liva app. DESIGN: This retrospective study serves as a secondary analysis of a randomised controlled trial, focusing on engagement patterns among participants in the intervention arm who received digital health coaching. The intervention group comprised participants enrolled in the Liva app, receiving mHealth lifestyle coaching. Our analysis concentrated on app usage data from 328 participants within the intervention group during the first phase of the study. METHODS: Principal component analysis reduced data to two dimensions, revealing principal components (PCs). A Gaussian mixture model clustered participants into distinct engagement patterns. RESULTS: Analysis of data from 328 pregnant women using the Liva app identified 3 distinct engagement clusters: Cluster 1, "Averagers"; Cluster 2, "Goalers"; and Cluster 3, "Immersers." These clusters correlated with two PCs. "Averagers" engaged moderately with both "Coach Features" and "Goal Features." "Goalers" predominantly used "Goal Features," while "Immersers" engaged with both "Coach Features" and "Goal Features." Notably, 82% of participants fell into the "Averagers" category. CONCLUSION: This study reveals that individuals, despite similar program participation under uniform conditions, engage with the program differently. Understanding these differences is essential to provide personalised support during pregnancy and has implications for tailored medicine, digital health, and intervention development. Further research is needed to validate these findings across diverse healthcare settings, exploring engagement patterns throughout different pregnancy phases and their impact on health outcomes.

摘要

背景：妊娠期糖尿病在全球范围内对孕期健康构成重大威胁，行为改变干预措施可有效降低风险。 目的：了解孕妇在移动健康（mHealth）干预中的不同参与模式对于个性化医疗至关重要。根据参与者的参与类型量身定制干预措施可提高项目效果。本研究旨在使用Liva应用程序探索有妊娠期糖尿病风险的孕妇的参与模式。 设计：本回顾性研究是对一项随机对照试验的二次分析，重点关注接受数字健康指导的干预组参与者的参与模式。干预组包括注册使用Liva应用程序并接受移动健康生活方式指导的参与者。我们的分析集中在研究第一阶段干预组内328名参与者的应用程序使用数据。 方法：主成分分析将数据降维至两个维度，揭示主成分（PC）。高斯混合模型将参与者聚类为不同的参与模式。 结果：对使用Liva应用程序的328名孕妇的数据进行分析，确定了3种不同的参与集群：集群1，“平均使用者”；集群2，“目标导向者”；集群3，“深度使用者”。这些集群与两个主成分相关。“平均使用者”对“教练功能”和“目标功能”的参与程度适中。“目标导向者”主要使用“目标功能”，而“深度使用者”则同时参与“教练功能”和“目标功能”。值得注意的是，82%的参与者属于“平均使用者”类别。 结论：本研究表明，尽管在统一条件下参与相同项目，但个体与项目的互动方式存在差异。了解这些差异对于孕期提供个性化支持至关重要，对个性化医疗、数字健康和干预措施开发具有重要意义。需要进一步研究以在不同医疗环境中验证这些发现，探索不同孕期阶段的参与模式及其对健康结果的影响。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3619/12141804/2b73ee5f901e/10.1177_17455057251327510-fig8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3619/12141804/6b6a62be5f47/10.1177_17455057251327510-fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3619/12141804/380363de2fd7/10.1177_17455057251327510-fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3619/12141804/4a65348037d3/10.1177_17455057251327510-fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3619/12141804/585e92ffa88b/10.1177_17455057251327510-fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3619/12141804/b83112d53b8a/10.1177_17455057251327510-fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3619/12141804/61f55e94d164/10.1177_17455057251327510-fig6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3619/12141804/f9de2ccbece4/10.1177_17455057251327510-fig7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3619/12141804/2b73ee5f901e/10.1177_17455057251327510-fig8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3619/12141804/6b6a62be5f47/10.1177_17455057251327510-fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3619/12141804/380363de2fd7/10.1177_17455057251327510-fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3619/12141804/4a65348037d3/10.1177_17455057251327510-fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3619/12141804/585e92ffa88b/10.1177_17455057251327510-fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3619/12141804/b83112d53b8a/10.1177_17455057251327510-fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3619/12141804/61f55e94d164/10.1177_17455057251327510-fig6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3619/12141804/f9de2ccbece4/10.1177_17455057251327510-fig7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3619/12141804/2b73ee5f901e/10.1177_17455057251327510-fig8.jpg

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本文引用的文献

[1]
Analysis of Patient Cues in Asynchronous Health Interactions: Pilot Study Combining Empathy Appraisal and Systemic Functional Linguistics.

JMIR Form Res. 2022-12-20

[2]
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J Telemed Telecare. 2022-12

[3]
A qualitative study of the impact of peer support on women's mental health treatment experiences during the perinatal period.

BMC Pregnancy Childbirth. 2022-9-6

[4]
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Nutrients. 2022-8-19

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J Med Internet Res. 2020-12-1

[9]
A Smartphone-Based Technique to Detect Dynamic User Preferences for Tailoring Behavioral Interventions: Observational Utility Study of Ecological Daily Needs Assessment.

JMIR Mhealth Uhealth. 2020-11-13

[10]
Drivers of weight loss in a CDC-recognized digital diabetes prevention program.

BMJ Open Diabetes Res Care. 2020-7

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