Optimizing efficiency and impact of digital health interventions for caregivers: A mixed methods approach

项目摘要/摘要 每六个美国成年人中就有一个为患有残疾的亲人提供护理，而这些家庭照顾者是 比普通人群更容易经历失眠和其他心理问题。多重 现有的、基于证据的数字健康干预措施可以有效地满足照顾者的心理社会需求，并 增加照顾者获得支持性护理的机会。例如，使用互联网的睡眠健康(SHUTI) 由co-I Ritterband开发的是一种NCI指定的研究测试干预措施，提供认知- 失眠的行为疗法。一个关键的翻译研究问题仍然是关于现有的基于证据的 像SHUTI这样的数字健康干预措施，即什么程度的定制是必要和充分的 对照顾者来说，这些干预措施的最佳参与度和有效性？为了解决这个研究问题， 我们将招募100名高强度失眠照顾者，完成失眠基线评估和 照顾环境。照顾者随后将在开放标签试验中获得对SHUTI的访问权限，然后完成后- 评估并根据他们与6个干预“核心”的接触程度进行分类：非 用户(即未完成的核)、未完成的用户(即1到3个核)和完全用户(即4到6个核)。 对于目标1，我们将测试SHUTI敬业度与照料环境的关联。首先，我们要测试一下 护理者与SHUTI的参与度(即，作为非用户与不完整用户与完全用户)相关联 与他们的用户特征(即照顾压力、自我效能和负罪感)和环境特征(即， 与护理对象的接近程度；护理对象的功能、认知和行为状态；护理任务；目标1a)。 其次，我们将从照顾者对以下问题的回应来描述他们参与SHUTI活动的障碍和动机 开放式调查，以及针对照顾者的量身定做如何改进吸收和使用(目标1b)。主题性 编码还将检查护理者的建议如何推广到其他基于证据的数字健康 干预措施和调查结果将使用综合成员检查进行验证。对于目标2，我们将测试 舒体对舒体靶向认知变化机制(即更适应的睡眠)的影响 信念、内化睡眠控制点)与照看相关使用者和 环境特征。这两个目标的发现不仅对于指导下一步的研究是必要的 专门为照顾者量身定做和测试SHUTI，但也是为了推动科学走向我们的长期 目标，即提高护理人员数字健康干预的质量和影响，同时减少 干预发展效率低下--这一目标被确定为当前护理研究的一个高度优先的目标。AS 这样的发现将可以在经过研究测试的干预计划中翻译，并具有重大承诺 减少为照顾者开发数字健康干预措施的低效，同时也增加干预措施 对这些服务不足的人群的影响和影响。

PROJECT SUMMARY/ABSTRACT One in six American adults provide care for a loved one with disabling illness, and these family caregivers are more likely to experience insomnia and other psychological concerns than the general population. Multiple existing, evidence-based digital health interventions may effectively address caregivers' psychosocial needs and increase caregivers' access to supportive care. For example, Sleep Healthy Using the Internet (SHUTi) developed by co-I Ritterband is an NCI-designated research-tested intervention that delivers cognitive- behavioral therapy for insomnia. A key translational research question remains about existing evidence-based digital health interventions like SHUTi, namely, what level of tailoring would be necessary and sufficient achieve optimal engagement with and efficacy of these interventions for caregivers? To address this research question, we will recruit 100 high-intensity caregivers with insomnia to complete a baseline assessment of insomnia and caregiving context. Caregivers will then receive access to SHUTi in an open-label trial, then complete post- assessment and be categorized according to their level of engagement with the 6 intervention “Cores”: non- users (i.e., completed no Cores), incomplete users (i.e., 1 to 3 Cores), and complete users (i.e., 4 to 6 Cores). For Aim 1, we will test the association of SHUTi engagement with caregiving context. First, we will test whether caregivers' engagement with SHUTi (i.e., being a non-user vs. incomplete user vs. complete user) is associated with their user characteristics (i.e., caregiving strain, self-efficacy, and guilt) and environment characteristics (i.e., proximity to care recipient; care recipient functional, cognitive, and behavioral status; caregiving tasks; Aim 1a). Second, we will describe caregivers' barriers to and motivations for SHUTi engagement from their responses to open-ended surveys, and how caregiver-specific tailoring may improve uptake and usage (Aim 1b). Thematic coding will also examine how caregivers' recommendations generalize to other evidence-based digital health interventions, and findings will be validated using synthesized member checking. For Aim 2, we will test whether the effects of SHUTi on known cognitive mechanisms of change targeted by SHUTi (i.e., more adaptive sleep beliefs, internalized sleep locus of control) are associated with differences in caregiving-related user and environment characteristics. Findings from these two aims are not only necessary to direct next research on tailoring and testing SHUTi for caregivers specifically, but also to advance the science towards our long-term goal, namely, to improve the quality and impact of digital health interventions for caregivers, while reducing intervention development inefficiency – a goal identified as a high priority for current caregiving research. As such, findings will be translatable across research-tested intervention programs and hold significant promise to reduce inefficiencies in developing digital health interventions for caregivers, while also increasing intervention impact and reach for this underserved population.