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

项目总结/摘要 六分之一的美国成年人为患有残疾疾病的亲人提供护理，这些家庭护理人员是 他们比普通人更容易失眠和其他心理问题。多 现有的基于证据的数字健康干预措施可以有效地满足护理人员的心理需求， 增加照顾者获得支助性护理的机会。例如，使用互联网的睡眠健康（SHUTi） Ritterband共同开发的是一种NCI指定的研究测试干预， 失眠症的行为疗法一个关键的转化研究问题仍然是现有的循证医学， 像SHUTi这样的数字健康干预措施，即什么程度的定制是必要的和足够的， 这些干预措施对照顾者的最佳参与和有效性？为了解决这个研究问题， 我们将招募100名患有失眠症的高强度护理人员，以完成失眠症的基线评估， 背景知识。然后，护理人员将在一项开放标签试验中获得SHUTi，然后完成术后 评估，并根据其参与6项干预措施“核心”的程度进行分类： 用户（即，完成的没有核心），不完整的用户（即，1到3个核心）和完全用户（即，4至6个核心）。 对于目标1，我们将测试SHUTi参与与学习背景的关联。首先，我们将测试 照顾者与SHUTi的参与（即，作为非用户vs.不完全用户vs.完全用户）相关联 利用它们的用户特性（即，压力、自我效能和内疚）和环境特征（即， 与被看护者的接近度;被看护者的功能、认知和行为状态;完成任务;目标1a）。 其次，我们将描述照顾者的障碍和动机，SHUTi参与从他们的反应， 开放式调查，以及针对特定人群的定制如何提高接受和使用率（目标1b）。专题 编码还将研究护理人员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 SHUTi对SHUTi所靶向的已知认知改变机制的影响（即，适应性睡眠 信念，内化的睡眠控制点）与睡眠相关用户的差异有关， 环境特征。这两个目标的发现不仅是指导下一个研究的必要条件， 专门为护理人员量身定制和测试SHUTi，同时也是为了推动科学朝着我们的长期目标发展。 目标，即提高数字健康干预措施对护理人员的质量和影响，同时减少 干预发展效率低下-这一目标被确定为目前正在进行的研究的一个高度优先事项。作为 这样，研究结果将在经过研究测试的干预计划中得到推广，并具有重大的前景， 减少为护理人员制定数字健康干预措施的效率低下，同时增加干预措施 影响和覆盖这些服务不足的人群。

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.