# Optimizing efficiency and impact of digital health interventions for caregivers: A mixed methods approach > **NIH NIH R21** · UNIVERSITY OF VIRGINIA · 2021 · $214,659 ## Abstract 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 prom... ## Key facts - **NIH application ID:** 10298118 - **Project number:** 1R21TR003522-01A1 - **Recipient organization:** UNIVERSITY OF VIRGINIA - **Principal Investigator:** Kelly McLean Shaffer - **Activity code:** R21 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2021 - **Award amount:** $214,659 - **Award type:** 1 - **Project period:** 2021-07-30 → 2023-06-30 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10298118 ## Citation > US National Institutes of Health, RePORTER application 10298118, Optimizing efficiency and impact of digital health interventions for caregivers: A mixed methods approach (1R21TR003522-01A1). Retrieved via AI Analytics 2026-06-12 from https://api.ai-analytics.org/grant/nih/10298118. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*