Background: The increasing development and use of digital health interventions requires good quality costing information to inform development and commissioning choices about resource allocation decisions. The Narrative Experiences Online (NEON) Intervention is a web-application that delivers recorded mental health recovery narratives to its users. Two randomized controlled trials are testing the NEON Intervention in people with experience of psychosis (NEON) and people experiencing non-psychosis mental health problems (NEON-O).
Aim: This study describes and estimates the cost components and total cost of developing and delivering the NEON Intervention.
Materials And Methods: Total costs for the NEON Trial (739 participants) and NEON-O Trial (1,024 participants) were estimated by: identifying resource use categories involved in intervention development and delivery; accurate measurement or estimation of resource use; and a valuation of resource use to generate overall costs, using relevant unit costs. Resource use categories were identified through consultation with literature, costing reporting standards and iterative consultation with health researchers involved in NEON Intervention development and delivery. Sensitivity analysis was used to test assumptions made.
Results: The total cost of developing the NEON Intervention was £182,851. The largest cost components were software development (27%); Lived Experience Advisory Panel workshops (23%); coding the narratives (9%); and researchers' time to source narratives (9%). The total cost of NEON Intervention delivery during the NEON Trial was £118,663 (£349 per NEON Intervention user). In the NEON-O Trial, the total delivery cost of the NEON Intervention was £123,444 (£241 per NEON Intervention user). The largest cost components include updating the narrative collection (50%); advertising (19%); administration (14%); and software maintenance (11%). Uncertainty in the cost of administration had the largest effect on delivery cost estimates.
Conclusion: Our work shows that developing and delivering a digital health intervention requires expertise and time commitment from a range of personnel. Teams developing digital narrative interventions need to allocate substantial resources to curating narrative collections.
Implications For Practice: This study identifies the development and delivery resource use categories of a digital health intervention to promote the consistent reporting of costs and informs future decision-making about the costs of delivering the NEON Intervention at scale.
Trial Registration: NEON Trial: ISRCTN11152837, registered 13 August 2018, http://www.isrctn.com/ISRCTN11152837. NEON-O Trial: ISRCTN63197153, registered 9 January 2020, http://www.isrctn.com/ISRCTN63197153.
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http://dx.doi.org/10.3389/fpsyt.2022.1028156 | DOI Listing |
Drug Alcohol Depend Rep
December 2024
University of Washington, School of Medicine, Division of Allergy and Infectious Diseases, Seattle, WA, United States.
Background: Syringe services programs (SSPs) serve as key platforms to deliver harm reduction services to people who use drugs (PWUD). Changes in drug supply and drug consumption behaviors, particularly the increasing use of fentanyl through non-injection methods, may impact SSP utilization.
Material And Methods: We collected routine program data from three SSPs in King County, Washington.
Mar Life Sci Technol
November 2024
College of Marine Sciences, Shanghai Ocean University, Shanghai, 201306 China.
Lung Cancer
November 2024
Massachusetts General Hospital Cancer Center and Department of Medicine, Massachusetts General Hospital, Boston, MA, USA; Novartis Institute of Biomedical Research, Boston, MA, USA.
Introduction: Anaplastic lymphoma kinase rearranged (ALK + ) lung cancers often develop ALK-independent resistance mechanisms that reactivate the mitogen-activated protein kinase pathway signaling pathway. We therefore evaluated alectinib combined with the MEK inhibitor cobimetinib in metastatic ALK + lung cancer.
Materials And Methods: This phase Ib study employed a 3 + 3 design.
Sci Rep
November 2024
Mechanical Engineering & Applied Mechanics, University of Pennsylvania, Philadelphia, PA, USA.
Operator learning is a rising field of scientific computing where inputs or outputs of a machine learning model are functions defined in infinite-dimensional spaces. In this paper, we introduce Neon (Neural Epistemic Operator Networks), an architecture for generating predictions with uncertainty using a single operator network backbone, which presents orders of magnitude less trainable parameters than deep ensembles of comparable performance. We showcase the utility of this method for sequential decision-making by examining the problem of composite Bayesian Optimization (BO), where we aim to optimize a function , where is an unknown map which outputs elements of a function space, and is a known and cheap-to-compute functional.
View Article and Find Full Text PDFJ Thorac Oncol
November 2024
Department of Haematology-Oncology, National University Cancer Institute Singapore, National University Health System, Singapore.
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