Evidence map›Paper›PMID 40679305›Full record

ArticleJournal of advanced nursing2026

Understanding Nurses' Needs Regarding Tailored, Evidence-Based Sleep Education and Training.

Beverly M Hittle, Imelda S Wong, Carolyn Smith, Kimberly A Honn, Sarah Hamill-Skoch, Angela Theil, Joshua Lambert, Gordon L Gillespie

Registry-linked trialAbstract read
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In one paragraph

Article in Journal of advanced nursing, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06105307 (Using Cognitive-Behavioral Change and Mobile Technology to Improve RN Sleep and Fatigue), which is not on this map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT06105307 naenrolling by invitationnot on this map

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

TypeinterventionalSponsorUniversity of CincinnatiRan2024 to 2027Enrolled76ConditionsOccupational Health, Sleep, Fatigue, NurseArmsRN-SLEEP, Healthy Habit - Educational Control
3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Beverly M HittleUniversity of Cincinnati, College of Nursing, Cincinnati, Ohio, USA.ORCID https://orcid.org/0000-0001-6327-3901
Imelda S WongBC Public Service, Office of the Provincial Health Officer, Victoria, British Columbia, Canada.ORCID https://orcid.org/0000-0003-4224-5092
Carolyn SmithUniversity of Cincinnati, College of Nursing, Cincinnati, Ohio, USA.ORCID https://orcid.org/0000-0001-9890-4726
Kimberly A HonnSleep and Performance Research Center and Department of Translational Medicine and Physiology, Washington State University, Spokane, Washington, USA.ORCID https://orcid.org/0000-0001-8911-6277
Sarah Hamill-SkochUniversity of Cincinnati, College of Medicine, Cincinnati, Ohio, USA.ORCID https://orcid.org/0000-0003-4323-2998
Angela TheilUniversity of Cincinnati, College of Nursing, Cincinnati, Ohio, USA.ORCID https://orcid.org/0009-0008-9277-181X
Joshua LambertUniversity of Cincinnati, College of Nursing, Cincinnati, Ohio, USA.ORCID https://orcid.org/0000-0002-4513-8156
Gordon L GillespieUniversity of Cincinnati, College of Nursing, Cincinnati, Ohio, USA.ORCID https://orcid.org/0000-0002-0231-1123

Funding

NIOSH CDC HHS #K01OH012549NIOSH CDC HHS K01 OH012549
6 · The paper itself

Abstract

aimIdentify desired training content for shift-working nurses to improve their sleep and fatigue.

designA descriptive qualitative design.

methodsWe recruited night shift nurses (N = 23) to provide feedback during virtual focus groups/interviews. Data collection occurred in the U.S. between March and June 2024. Participants were presented with sleep and fatigue topics derived from the literature. Focus group/interview data were collected and transcribed. Data were analysed using a hybrid deductive-inductive manifest content analysis with an a priori coding schema based on topics shared during data collection. Data not fitting the schema, yet informing content, were analysed inductively.

resultsThree themes aligned with literature-derived topics. Theme 1, Why We Sleep and Why Should Nurses Care, explains the importance of sleep to health. Theme 2, Sleep Practices for Nurses to Support Health and Social Relationships, describes healthy strategies to promote sleep for enhanced quality of life. Theme 3, Fatigue and Work, illustrates the significance of nurse sleep and fatigue risk mitigation to safe working conditions and patient care.

conclusionsStudy findings highlight night shift nurses' interest in gaining evidence-based information to promote their sleep. Sleep education and training could fill a knowledge and skills gap, not often offered in school or workplace. IMPLICATIONS FOR THE PROFESSION: Identifying themes relevant to nurses may help increase the development and availability of sleep education and training currently tailored for nurses. IMPACT: Study findings describe content night shift nurses' desire for sleep and fatigue training, serving as an important first step in developing programmes most relevant to shift-working nurses. Our analysis found the findings largely align with key components workers should receive in sleep education and training and reinforced the need for employers to offer such training. This study could benefit the nursing workforce and employers who expect rested, high-functioning nurses to care for patients. REPORTING

methodStandards for Reporting Qualitative Research. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution. TRIAL AND PROTOCOL REGISTRATION: Clinicaltrials.gov, NCT06105307.

Indexed as

FatigueNursing Staff, HospitalSleepWork Schedule ToleranceAdultFemaleFocus GroupsHumansMaleMiddle AgedQualitative Researchfatigue risk mitigationhealthcare workeroccupational health and safetypatient safetysleep education and training

Identifiers

PMID40679305
PMCPMC12326336

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.