■移动健康(mHealth)具有从根本上改善健康行为和生活质量的潜力;但是,在了解如何优化移动健康参与方面仍然存在关键差距。大多数参与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,认知投资对于有意义的行为改变也可能很重要。值得注意的是,设计为要求过多反射的内容可能会导致用户脱离接触。了解如何在反应负担和反思负担之间取得平衡,对于实现有效参与以影响预期结果至关重要。
■在这项观察性研究中,我们试图了解反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。具体来说,我们探讨了mHealth内容的响应和反射负担的变化将如何影响mHealth干预中用户的短信响应率。
■我们招募了糖尿病患者的支持人员进行一项随机对照试验,该试验评估了mHealth干预对糖尿病管理的影响。分配给干预的支持人员(n=148)完成了一项调查,并收到了9个月的短信。在为期2年的随机对照试验中,我们发送了4个版本的周刊,双向文本消息在反射负担(相对于其他消息的认知反射要求的水平)和响应负担(相对于其他消息的响应要求的信息水平)方面都有所不同。我们通过使用参与者级别的响应率来量化参与度。我们比较了每个文本的响应几率,并使用泊松回归来估计参与者特征和响应率之间的关联。
■无论响应负担如何,要求最多反思的文本的响应率最低(高反思和低响应负担:中位数为10%,IQR0%-40%;高反射和高响应负担:中位数23%,IQR0%-51%)。要求最少反射的文本的响应率最高(低反射和低响应负担:中位数90%,IQR61%-100%)对于要求中等反射的文本(中等反射和低响应负担:中位数75%,IQR38%-96%)。较低的反应几率与较高的反射负担相关(P<.001)。年轻的参与者和具有较低社会经济地位的参与者对具有更多反思负担的文本的响应率较低,相对于它们的对应物(所有P值均<.05)。
■随着反射负担的增加,参与度下降,我们发现不同参与者特征的参与度存在更多差异。鼓励适度反思的内容可能是实现认知投资和系统使用的理想选择。我们的发现为mHealth设计以及参与度和有效性的优化提供了见解。
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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).
UNASSIGNED: 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.