Objective: To test whether an interactive voice response (IVR) system phone call was equally effective as a nurse-delivered phone call at educating and preparing patients for flexible sigmoidoscopy (FS) and colonoscopy examinations.
Study Design: Three-arm randomized controlled trial.
Methods: The trial included patients with upcoming FS or colonoscopy appointments to test the equivalence of an IVR system to nurse-delivered phone calls in reducing appointment nonattendance and inadequate preparation for an examination. Message timing and satisfaction with the intervention were assessed. The 3 study conditions included the following: nurse phone call 7 days before the procedure, IVR system call 7 days before the procedure, and IVR system call 3 days before the procedure. All calls included an appointment reminder, information about preparation for the examination, and encouragement to prepare for and attend the examination.
Results: A total of 3610 patients were eligible for the study; of these, 1229 (34%) were scheduled for FS and 2381 (66%) for colonoscopy. There were no statistically significant differences across the 3 study arms in appointment attendance or adherence to preparation instructions. Significantly more patients in IVR conditions reported neutral perceptions about the phone calls, and more patients receiving nurse calls reported very positive perceptions about the phone calls.
Conclusion: An IVR system call is as effective as a nurse phone call for ensuring that patients attend appointments and are adequately prepared for endoscopy examinations.
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Front Psychol
December 2024
Department of Psychology, Faculty of Arts, Masaryk University, Brno, Czechia.
Immersive Virtual Reality (iVR) presents a promising avenue for treating acrophobia through Virtual Reality Exposure Therapy (VRET). This paper explores the current state of VRET for acrophobia, identifying significant technological and practical barriers that limit its effectiveness and hinder widespread adoption. Key challenges include the need for more advanced and realistic user experiences, and for the integration of biofeedback mechanisms.
View Article and Find Full Text PDFDiagnostics (Basel)
November 2024
Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA 91125, USA.
: The research addresses algorithmic bias in deep learning models for cardiovascular risk prediction, focusing on fairness across demographic and socioeconomic groups to mitigate health disparities. It integrates fairness-aware algorithms, susceptible carrier-infected-recovered (SCIR) models, and interpretability frameworks to combine fairness with actionable AI insights supported by robust segmentation and classification metrics. : The research utilised quantitative 3D/4D heart magnetic resonance imaging and tabular datasets from the Cardiac Atlas Project's (CAP) open challenges to explore AI-driven methodologies for mitigating algorithmic bias in cardiac imaging.
View Article and Find Full Text PDFBMC Glob Public Health
June 2024
School of Public Health, College of Health Sciences, Addis Ababa University, Addis Ababa, Ethiopia.
Background: In response to the COVID-19 challenge and the consequent concerns and misconceptions about potential mother-to-child virus transmission, the United Nations Children's Fund (UNICEF), in collaboration with the Ethiopian Ministry of Health, launched a 3-month nationwide media campaign to promote appropriate and safe breastfeeding practices using national and regional television and radio channels, as well as social media. This study assesses the reach and impact of a media campaign in Ethiopia on improving mothers', partners'/caregivers', and the public's awareness of and practices related to appropriate and safe breastfeeding.
Methods: A two-round mobile survey was conducted using random digit dialing (RDD) and an interactive voice response (IVR) system.
J Chem Phys
December 2024
Pitaevskii BEC Center, CNR-INO and Dipartimento di Fisica, Università di Trento, Via Sommarive 14, Trento I-38123, Italy.
Nonadiabatic quantum-classical mapping approaches have significantly gained in popularity over the past several decades because they have acceptable accuracy while remaining numerically tractable even for large system sizes. In the recent few years, several novel mapping approaches have been developed that display higher accuracy than the traditional Ehrenfest method, linearized semiclassical initial value representation (LSC-IVR), and Poisson bracket mapping equation (PBME) approaches. While various benchmarks have already demonstrated the advantages and limitations of those methods, unified theoretical justifications of their short-time accuracy are still demanded.
View Article and Find Full Text PDFChemphyschem
December 2024
Clausius-Institut für Physikalische und Theoretische Chemie, Rheinische Friedrich-Wilhelms-Universität Bonn, Wegelerstrasse 12, 53115, Bonn.
Infrared probes are chemical moieties whose vibrational modes are used to obtain spectroscopic information about structural dynamics of complex systems; in particular, of biomacromolecules. Here, we explore the vibrational spectroscopy and dynamics of a reagent, 3-(4-azidophenyl)propiolonitrile (AzPPN), for selectively tagging thiols in protein environments with a multifunctional infrared probe containing both, an azide and a nitrile chromophore. The linear infrared spectrum of AzPPN is heavily perturbed in the antisymmetric azide stretching region as a result of accidental Fermi resonances.
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