Dropping out of a peripartum depression mHealth study: participants' motives and suggestions for improvement.

IF 3.9 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES BMC Medical Research Methodology Pub Date : 2025-01-11 DOI:10.1186/s12874-025-02462-z
Hanna Wierenga, Konstantina V Pagoni, Alkistis Skalkidou, Fotios C Papadopoulos, Femke Geusens
{"title":"Dropping out of a peripartum depression mHealth study: participants' motives and suggestions for improvement.","authors":"Hanna Wierenga, Konstantina V Pagoni, Alkistis Skalkidou, Fotios C Papadopoulos, Femke Geusens","doi":"10.1186/s12874-025-02462-z","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Peripartum depression is a common but potentially debilitating pregnancy complication. Mobile applications can be used to collect data throughout the pregnancy and postpartum period to improve understanding of early risk indicators.</p><p><strong>Aim: </strong>This study aimed to improve understanding of why women drop out of a peripartum depression mHealth study, and how we can improve the app design.</p><p><strong>Method: </strong>Participants who dropped out of the Mom2B study (n = 134) answered closed and open questions on their motives for dropping out of the study, suggestions for improvement, and preferred timeframe of the study. A mix of quantitative and qualitative strategies was used to analyze the responses.</p><p><strong>Results: </strong>The most common reasons for discontinuation were lack of time, problems with or loss of the pregnancy, the use of other pregnancy applications, surveys being too lengthy, the app draining too much battery, and participants incorrectly believing that their answers were irrelevant for the study. Participants suggested fewer survey moments, more reminders, and a need for more unique content compared to commercially available apps.</p><p><strong>Conclusions: </strong>Researcher who want to use mHealth designs in peripartum studies need to ensure that their study designs are as time-efficient as possible, remind participants about the study, manage expectations about the study and what is expected of participants throughout the study, design their apps to be attractive in a competitive market, and follow-up with participants who are excluded from the study due to pregnancy complications.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"25 1","pages":"6"},"PeriodicalIF":3.9000,"publicationDate":"2025-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11724601/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"BMC Medical Research Methodology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s12874-025-02462-z","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}
引用次数: 0

Abstract

Background: Peripartum depression is a common but potentially debilitating pregnancy complication. Mobile applications can be used to collect data throughout the pregnancy and postpartum period to improve understanding of early risk indicators.

Aim: This study aimed to improve understanding of why women drop out of a peripartum depression mHealth study, and how we can improve the app design.

Method: Participants who dropped out of the Mom2B study (n = 134) answered closed and open questions on their motives for dropping out of the study, suggestions for improvement, and preferred timeframe of the study. A mix of quantitative and qualitative strategies was used to analyze the responses.

Results: The most common reasons for discontinuation were lack of time, problems with or loss of the pregnancy, the use of other pregnancy applications, surveys being too lengthy, the app draining too much battery, and participants incorrectly believing that their answers were irrelevant for the study. Participants suggested fewer survey moments, more reminders, and a need for more unique content compared to commercially available apps.

Conclusions: Researcher who want to use mHealth designs in peripartum studies need to ensure that their study designs are as time-efficient as possible, remind participants about the study, manage expectations about the study and what is expected of participants throughout the study, design their apps to be attractive in a competitive market, and follow-up with participants who are excluded from the study due to pregnancy complications.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
退出围产期抑郁症移动健康研究:参与者的动机和改善建议。
背景:围产期抑郁是一种常见但潜在的使人衰弱的妊娠并发症。移动应用程序可用于收集整个孕期和产后期间的数据,以提高对早期风险指标的了解。目的:本研究旨在提高对女性退出围产期抑郁症移动健康研究的理解,以及我们如何改进应用程序设计。方法:退出Mom2B研究的参与者(n = 134)回答了关于他们退出研究的动机、改进建议和首选研究时间框架的封闭式和开放式问题。定量和定性策略的混合使用来分析回应。结果:最常见的中断原因是缺乏时间,怀孕问题或流产,使用其他怀孕应用程序,调查太长,应用程序消耗太多电池,以及参与者错误地认为他们的答案与研究无关。与商业应用程序相比,参与者建议减少调查时刻,增加提醒,需要更多独特的内容。结论:想要在围产期研究中使用移动健康设计的研究人员需要确保他们的研究设计尽可能具有时间效率,提醒参与者有关研究,管理对研究的期望以及在整个研究过程中对参与者的期望,设计他们的应用程序在竞争激烈的市场中具有吸引力,并对因妊娠并发症而被排除在研究之外的参与者进行随访。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
BMC Medical Research Methodology
BMC Medical Research Methodology 医学-卫生保健
CiteScore
6.50
自引率
2.50%
发文量
298
审稿时长
3-8 weeks
期刊介绍: BMC Medical Research Methodology is an open access journal publishing original peer-reviewed research articles in methodological approaches to healthcare research. Articles on the methodology of epidemiological research, clinical trials and meta-analysis/systematic review are particularly encouraged, as are empirical studies of the associations between choice of methodology and study outcomes. BMC Medical Research Methodology does not aim to publish articles describing scientific methods or techniques: these should be directed to the BMC journal covering the relevant biomedical subject area.
期刊最新文献
A generative model for evaluating missing data methods in large epidemiological cohorts. Discrepancies in safety reporting for chronic back pain clinical trials: an observational study from ClinicalTrials.gov and publications. Multiple states clustering analysis (MSCA), an unsupervised approach to multiple time-to-event electronic health records applied to multimorbidity associated with myocardial infarction. Matching plus regression adjustment for the estimation of the average treatment effect on survival outcomes: a case study with mosunetuzumab in relapsed/refractory follicular lymphoma. Protocol publication rate and comparison between article, registry and protocol in RCTs.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1