Sleep disturbances and circadian disruption play a central role in adverse health, safety, and performance outcomes in shift workers. While biomathematical models of sleep and alertness can be used to personalise interventions for shift workers, their practical implementation is undertested. This study tested the feasibility of implementing two biomathematical models-the Phillips-Robinson Model and the Model for Arousal Dynamics-in 28 shift-working nurses, 14 in each group. The study examined the overlap and adherence between model recommendations and sleep behaviours, and changes in sleep following the implementation of recommendations. For both groups combined, the mean (SD) percentage overlap between when a model recommended an individual to sleep and when sleep was obtained was 73.62% (10.24%). Adherence between model recommendations and sleep onset and offset times was significantly higher with the Model of Arousal Dynamics compared to the Phillips-Robinson Model. For the Phillips-Robinson model, 27% of sleep onset and 35% of sleep offset times were within ± 30 min of model recommendations. For the Model of Arousal Dynamics, 49% of sleep onset, and 35% of sleep offset times were within ± 30 min of model recommendations. Compared to pre-study, significant improvements were observed post-study for sleep disturbance (Phillips-Robinson Model), and insomnia severity and sleep-related impairments (Model of Arousal Dynamics). Participants reported that using a digital, automated format for the delivery of sleep recommendations would enable greater uptake. These findings provide a positive proof-of-concept for using biomathematical models to recommend sleep in operational contexts.

Download full-text PDF

Source
http://dx.doi.org/10.1111/jsr.14026DOI Listing

Publication Analysis

Top Keywords

model recommendations
20
phillips-robinson model
16
model arousal
16
sleep
15
model
14
shift workers
12
sleep onset
12
offset times
12
arousal dynamics
12
adherence model
8

Similar Publications

Background: Diffusing alpha-emitters Radiation Therapy ("Alpha DaRT") is a promising new radiation therapy modality for treating bulky tumors. Ra-carrying sources are inserted intratumorally, producing a therapeutic alpha-dose region with a total size of a few millimeter via the diffusive motion of Ra's alpha-emitting daughters. Clinical studies of Alpha DaRT have reported 100% positive response (30%-100% shrinkage within several weeks), with post-insertion swelling in close to half of the cases.

View Article and Find Full Text PDF

Background: Active surveillance (AS) is the guideline-recommended treatment for low-risk prostate cancer and involves routine provider visits, lab tests, imaging, and prostate biopsies. Despite good uptake, adherence to AS, in terms of receiving recommended follow-up testing and remaining on AS in the absence of evidence of cancer progression, remains challenging.

Objective: We sought to better understand urologist, primary care providers (PCPs), and patient experiences with AS care delivery to identify opportunities to improve adherence.

View Article and Find Full Text PDF

Publicly available trial matching tools can improve the access to therapeutic innovations, but errors may expose to over-solicitation and disappointment. We performed a pragmatic non-interventional prospective evaluation on sequential patients at the Molecular Tumor Board of Centre Leon Berard. During 10 weeks in 2024, we analysed 157 patients with four clinical trial matching tools from the 19 screened: Klineo, ScreenAct, Trialing and DigitalECMT.

View Article and Find Full Text PDF

Urban pandemic governance personal protective equipment allocation strategies: a system dynamics simulation.

Sci Rep

January 2025

Institute for Disaster Management and Reconstruction, Sichuan University, No. 122, Section 1, Huanghe Middle Road, Chengdu, 610211, China.

In the early days of the urban pandemic, many cities had personal protective equipment (PPE) shortages, which adversely affected urban pandemic governance. Using the COVID-19 strategies employed in Wuhan as the pivotal case study, this study sought to determine effective strategies to optimize city PPE distribution. System dynamics modeling was employed to explore the influence of PPE allocation strategies on pandemic control measures.

View Article and Find Full Text PDF

The Application of Machine Learning Algorithms to Predict HIV Testing in Repeated Adult Population-Based Surveys in South Africa: Protocol for a Multiwave Cross-Sectional Analysis.

JMIR Res Protoc

January 2025

South African Medical Research Council/University of Johannesburg Pan African Centre for Epidemics Research Extramural Unit, Faculty of Health Sciences, University of Johannesburg, Johannesburg, South Africa.

Background: HIV testing is the cornerstone of HIV prevention and a pivotal step in realizing the Joint United Nations Program on HIV/AIDS (UNAIDS) goal of ending AIDS by 2030. Despite the availability of relevant survey data, there exists a research gap in using machine learning (ML) to analyze and predict HIV testing among adults in South Africa. Further investigation is needed to bridge this knowledge gap and inform evidence-based interventions to improve HIV testing.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!