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

The U.S. registered nurse (RN) workforce is the largest in the Healthcare and Social Assistance Sector and is at high risk for injuries and errors due to poor sleep and fatigue. Shift work (i.e., nights, evenings, rotating shifts) can contribute to RNs not obtaining adequate, restful sleep. Work intensity, including heavy physical and emotional workloads of caring for critically ill patients, can contribute to job stress, resulting in spill-over effects at home when RNs experience difficulties falling and staying asleep. To address work and home sleep barriers, this project proposes the development and pilot testing of RN-SLEEP, a skill-building mobile application designed to improve sleep. RN-SLEEP will provide a convenient, flexible space to learn sleep-enhancing evidence-based shift work-specific strategies, and cognitive-behavioral methods, (e.g., goal setting, relaxation training). Using NIOSH's Research 2 Practice (R2P) approach, the study team will collaborate with participants (N=18-24) from an RN union to refine RN-SLEEP content, integrating current sleep literature (including National Institute for Occupational Safety and Health \[NIOSH\] material) with cognitive-behavioral based training. RN-SLEEP will be pilot-tested using a two-group pretest-posttest study design, comparing sleep outcome measures (duration, quality) of RN-SLEEP participant users (n=38) with participants from an education control group (n=38). Data trends on fatigue, what drives behavior change (beliefs and self-efficacy), and other sleep outcome measures (timing, regularity, efficiency, daytime sleepiness) will be explored. RN-SLEEP goals align with Healthy People 2030, NIOSH's strategic goal to promote safe and healthy work design and well-being through two NIOSH Healthcare and Social Assistance Sector/Healthy Work Design Cross-Sector (HCSA/HWD) intermediate goals. HWD goal 7.2A is to conduct intervention research addressing fatigue (poor sleep sequela) due to suboptimal work designs (shift work) in the healthcare industry. HCSA/HWD goal 7.12A prioritizes interventions designed to impact work and non-work contributors to safety and health. This RN-SLEEP intervention aims to improve sleep by building skills that help RNs overcome obstacles to sleep from work and home, thus improving health and safety. Immediate outputs include a mobile app, designed and tested in collaboration with RNs, to improve sleep. Study results will be disseminated through our union collaborators, nursing conferences and journal publications, and our University's NIOSH-sponsored Education and Research Center social media outlets. Intermediate outcomes include enhancing RN sleep through training rarely available in nursing schools and traditional hospital health and safety training programs. Improving sleep can reduce fatigue and may decrease occupational injuries and errors. RN-SLEEP is adaptable, where future versions could be modified to meet the needs of other HCSA workers (i.e., nursing aides) and workers in other industries (e.g., oil and gas) scheduled to work non-standard work hours. End outcomes include integrating RN-SLEEP into a broader hospital organization intervention to mitigate fatigue risks.

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.

Eligibility Criteria:
Inclusion criteria include RNs:

* working night shift for a minimum of 6-months
* have access to a smart phone.

Exclusion criteria include RNs:

* who are pregnant or the parent of child(ren) less than 1 year of age
* who have a diagnosed sleep disorder.