Using Cognitive-Behavioral Change and Mobile Technology to Improve RN Sleep and Fatigue

The majority of RNs in the U.S. work in hospitals and post-acute care centers where their sleep could be compromised as a result of shift work (i.e., work outside 7 a.m. to 6 p.m.) and long work hours. RNs also face tremendous job stress when caring for critically ill patients, which can disrupt sleep. Sleep health is defined by multiple components including sleep timing, regularity, efficiency, duration, quality, and daytime sleepiness. When RNs experience poor sleep they are at increased risk for outcomes that have negative health and safety concerns for the RNs (e.g., fatigue, chronic disease development, on-the-job injuries) and for their patients. Despite these risks, RNs are reporting poor sleep quality and shorter sleep duration than what is recommended 7-9 hours/24-hour. Some research has been conducted that shows when RNs obtain training on sleep strategies (e.g., pre-shift naps) related to working shift work, they see an improvement in their sleep. While helpful, RNs may further benefit from a more holistic training approach to address the wider barriers to sleep, such as job stress. A holistic sleep approach can provide RNs with evidence-based skills to cope with shift adaptation and psychological sleep barriers while motivating RNs with encouraging behavior change strategies (e.g., goal setting). As a result, RNs may see an improvement in sleep health and decreased fatigue. This project proposes to develop a sleep training program designed to meet the needs of RNs working shift work. As such, the training program, RN-SLEEP, would include shift work strategies, basics of sleep science and physiology, behavior change components, and strategies found in an effective behavioral sleep medicine treatment known as cognitive behavioral therapy for insomnia (CBT-I). The candidate, Dr. Hittle, has expertise in occupational health and safety, including sleep and shift work in the healthcare industry. Dr. Hittle has assembled a mentoring team of experts to guide her training in cognitive-behavioral sleep methods for integration into worker sleep interventions, intervention research with a focus on mHealth, and implementation science using a Total Worker Health approach. Dr. Hittle's short-term goal is to become an independent occupational health and safety scientist skilled in the use of methodologies and techniques required for intervention research and successful implementation in the workplace. This K01 proposal serves as an opportunity for Dr. Hittle to gain these skills and build a body of research focused on sleep training for workers. The resources, time, and, materials needed for this project are available through the University of Cincinnati (UC), the College of Nursing, and resources from Dr. Wong (Primary Mentor) at the National Institute for Occupational Safety and Health. Other UC-based resources include UCIT which will support Dr. Hittle's mobile application development and the Center for Clinical \& Translational Training and Science which offers services for K01 awardees in an effort to foster junior researchers. This project has two aims: 1. To refine a mobile application, RN-SLEEP, to determine the training components of most interest to RNs when looking to improve their sleep. 2. To pilot test RN-SLEEP with a pretest-posttest, repeated measure study design to measure RN participant engagement with RN-SLEEP, appeal (e.g., aesthetics, ease of use), and the usefulness of the training contents to improve RN sleep over the study period versus an educational training on healthy living. This pilot test will also help the researchers better understand the functionality of study activities. For aim 1, RN-SLEEP content refinement: This qualitative component of the project will recruit 18-24 participants for focus group data collection. Participants will be recruited using a convenience sampling method and will be assigned as recruited (non-randomized) to one of three focus groups. Six to eight participants will be in each focus group. The first set of focus groups will be conducted to determine the best content to include in RN-SLEEP. RN-SLEEP content will be refined. Then, a second set of focus groups will occur, sharing the updated RN-SLEEP with participants for final feedback. Focus group sample sizes were determined based on the literature. Focus group data will be analyzed using a modified constant comparative analysis method. For aim 2, RN-SLEEP intervention will be pilot-tested. Method for assigning participants to intervention versus control groups: Once participants are determined to be eligible for the study and informed consent is signed, the investigators will randomize participants to the RN-SLEEP intervention or educational control group. The investigators will use REDCap, a data management platform, to randomly assign participants, stratifying groups by self-reported sex. Method for delivering the intervention: Baseline measures will be collected. Participants will then be asked to engage daily with the RN-SLEEP or educational control group, based on their assigned group, for four weeks. Post-intervention measures will be collected 4 and 8-weeks after the intervention period is complete. Method for sample size determination: Our goal for recruitment is 76 participants (38 for each group). The investigators determined our sample size based on power calculations for our primary outcome measures (sleep duration and sleep quality) and based on the literature and previous work by the PI. The investigators increased the power calculation total sample goal of 58 total participants by 30% to account for any participants lost to attrition. Method for data analysis: Primary outcome measures (sleep duration, sleep quality): Descriptive statistics (i.e., means, 95% confidence intervals) will be used to describe characteristics and outcome variables for the overall study sample and each study group (intervention and education control). Between group differences for sleep duration and quality will be assessed using a two group independent t-test or Mann-Whitney U (if non-parametric testing needed). If statistical differences are noted between sleep duration and quality, appropriate statistical analyses will be completed with positively correlating covariates included in model. Secondary outcome measures: Fatigue, other measures of Sleep Health (timing, regularity, efficiency, daytime sleepiness), Sleep efficacy and beliefs Descriptive statistics on fatigue, sleep beliefs, self-efficacy and remaining sleep health measures will be explored for pre-and-post-training data trends for the intervention and control groups. Secondary outcome measures: Mobile App metrics for Acceptability, Usability, and Engagement The investigators will compute and report descriptive statistics on 4 and 8-week post-training acceptability, usability, and app engagement measures. Other outcome measures: study feasibility measures The investigators will be monitoring recruitment and retention statistics throughout the study. The investigators will report on screened, screened eligible, and enrolled participants, and participant retention rates at 4 and 8-week post-training data collection.