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There is evidence that survivors of childhood cancer have a high prevalence of poor sleep, including symptoms of insomnia. Insomnia is highly comorbid and has been associated with impaired cognitive performance, a range of psychiatric disorders, cardiovascular disease, and reduced quality of life. However, we still lack knowledge about the direct impact of available internet-based insomnia treatment programs for survivors of childhood cancer experiencing insomnia, in addition to how improving insomnia symptoms impacts neurocognitive function and late health morbidities in this population. Therefore, in this study, we will utilize the resources available in the Childhood Cancer Survivor Study (CCSS) to use an accepted, established, efficacious internet-delivered CBTi insomnia treatment program and evaluate the efficacy of this program in adult survivors of childhood cancer. Positive results from this study and our use of an internet-based intervention are likely generalizable and be scalable to the large and geographically diverse population of childhood cancer survivors with chronic health conditions. Primary Objective To examine the efficacy of an eHealth intervention for improving symptoms of insomnia among adult survivors of childhood cancer. Secondary Objectives To examine the impact of an eHealth intervention for insomnia on the clinical severity of insomnia symptoms in adult survivors of childhood cancer. To determine whether treatment of insomnia symptoms will improve neurocognitive function in adult survivors of childhood cancer with both insomnia and neurocognitive impairment. To explore the mediating effects of improved neurocognitive function, emotional distress, and cardiovascular health on the association between insomnia symptoms and quality of life.
To investigate factors that predict cognitive enhancement following engagement in an intensive Computerized Cognitive Training Protocol.
This study leverages a modernized digital version of a well-known cognitive screening tool to examine pre and post operative cognitive function after surgery in adults age 65 years or more. Machine learning algorithms will be applied to the hospital wide standard of care cognitive metric to identify risk for post-operative cognitive complications.