Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States.
Center for Health Behavior and Health Education, Vanderbilt University Medical Center, Nashville, TN, United States.
JMIR Mhealth Uhealth. 2024 Jan 19;12:e47632. doi: 10.2196/47632.
Mobile health (mHealth) has the potential to radically improve health behaviors and quality of life; however, there are still key gaps in understanding how to optimize mHealth engagement. Most engagement research reports only on system use without consideration of whether the user is reflecting on the content cognitively. Although interactions with mHealth are critical, cognitive investment may also be important for meaningful behavior change. Notably, content that is designed to request too much reflection could result in users' disengagement. Understanding how to strike the balance between response burden and reflection burden has critical implications for achieving effective engagement to impact intended outcomes.
In this observational study, we sought to understand the interplay between response burden and reflection burden and how they impact mHealth engagement. Specifically, we explored how varying the response and reflection burdens of mHealth content would impact users' text message response rates in an mHealth intervention.
We recruited support persons of people with diabetes for a randomized controlled trial that evaluated an mHealth intervention for diabetes management. Support person participants assigned to the intervention (n=148) completed a survey and received text messages for 9 months. During the 2-year randomized controlled trial, we sent 4 versions of a weekly, two-way text message that varied in both reflection burden (level of cognitive reflection requested relative to that of other messages) and response burden (level of information requested for the response relative to that of other messages). We quantified engagement by using participant-level response rates. We compared the odds of responding to each text and used Poisson regression to estimate associations between participant characteristics and response rates.
The texts requesting the most reflection had the lowest response rates regardless of response burden (high reflection and low response burdens: median 10%, IQR 0%-40%; high reflection and high response burdens: median 23%, IQR 0%-51%). The response rate was highest for the text requesting the least reflection (low reflection and low response burdens: median 90%, IQR 61%-100%) yet still relatively high for the text requesting medium reflection (medium reflection and low response burdens: median 75%, IQR 38%-96%). Lower odds of responding were associated with higher reflection burden (P<.001). Younger participants and participants who had a lower socioeconomic status had lower response rates to texts with more reflection burden, relative to those of their counterparts (all P values were <.05).
As reflection burden increased, engagement decreased, and we found more disparities in engagement across participants' characteristics. Content encouraging moderate levels of reflection may be ideal for achieving both cognitive investment and system use. Our findings provide insights into mHealth design and the optimization of both engagement and effectiveness.
移动健康（mHealth）有可能从根本上改善健康行为和生活质量；然而，在如何优化 mHealth 参与度方面，仍存在关键的认识差距。大多数参与度研究仅报告系统使用情况，而不考虑用户是否在认知上反思内容。尽管与 mHealth 的互动至关重要，但认知投入对于有意义的行为改变也可能很重要。值得注意的是，设计要求过多反思的内容可能会导致用户失去参与度。了解如何在响应负担和反思负担之间取得平衡，对于实现有效的参与度以产生预期的结果具有重要意义。
在这项观察性研究中，我们试图了解响应负担和反思负担之间的相互作用以及它们如何影响 mHealth 参与度。具体而言，我们探讨了改变 mHealth 内容的响应和反思负担如何影响 mHealth 干预措施中用户的短信回复率。
我们招募了糖尿病患者的支持人员参加一项随机对照试验，评估了一种用于糖尿病管理的 mHealth 干预措施。被分配到干预组的支持人员参与者（n=148）完成了一项调查，并在 9 个月内收到了短信。在为期 2 年的随机对照试验期间，我们发送了 4 种每周两次的短信，这些短信在反思负担（相对于其他短信的认知反思请求水平）和响应负担（相对于其他短信的响应请求信息水平）方面都有所不同。我们通过参与者层面的回复率来衡量参与度。我们比较了每条短信的回复几率，并使用泊松回归来估计参与者特征与回复率之间的关联。
无论响应负担如何，请求最多反思的短信回复率最低（高反思和低响应负担：中位数 10%，IQR 0%-40%；高反思和高响应负担：中位数 23%，IQR 0%-51%）。请求最少反思的短信回复率最高（低反思和低响应负担：中位数 90%，IQR 61%-100%），而请求中等反思的短信回复率仍然相对较高（中等反思和低响应负担：中位数 75%，IQR 38%-96%）。与较低的响应负担相比，较低的响应几率与较高的反思负担相关（P<.001）。年轻的参与者和社会经济地位较低的参与者对具有较高反思负担的短信的回复率较低，而与他们的同龄人相比（所有 P 值均<.05）。
随着反思负担的增加，参与度下降，并且我们在参与者特征方面发现了更大的参与度差距。鼓励适度反思的内容可能是实现认知投入和系统使用的理想选择。我们的研究结果为 mHealth 设计以及参与度和有效性的优化提供了见解。
