Background: It is recognized that a significant proportion of people with depression are prone to relapse, even after successful treatment, and that self-management interventions should be developed and provided. There is evidence that implementation intentions (IMPS) can be successfully applied to health-related behaviours but their application to self-management of mental health problems has been limited.
Aims: This paper describes the design and initial evaluation of a Self-Management After Therapy (SMArT) intervention, which incorporated IMPS and followed psychological therapy for depression. We sought to assess the feasibility and acceptability of SMArT.
Method: The SMArT intervention was designed with reference to the MRC guidance on developing and evaluating complex interventions and co-designed with and implemented in a UK Improving Access to Psychological Therapies (IAPT) service. Eleven patients who were in remission following treatment for depression received the SMArT intervention, provided by Psychological Wellbeing Practitioners (PWPs). The evaluation used routine IAPT outcome measures at each session, feedback from patients and PWPs, and analysis of the type of IMPS identified and their fidelity with the model. Six patients provided brief feedback about the intervention to an independent researcher.
Results: Feedback from patients and PWPs suggested that the intervention was feasible, acceptable and could potentially help patients to stay well after therapy. Patients confirmed the value of setting their own goals in the form of IMPS, receiving support from PWPs and in some cases from partners, friends and family members.
Conclusions: Implementation intentions are a promising approach to support the self-management of depression.
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http://dx.doi.org/10.1017/S1352465818000255 | DOI Listing |
Sci Rep
January 2025
Computer Science Department, Faculty of Computers and Information, South Valley University, Qena, 83523, Egypt.
Adversarial attacks were commonly considered in computer vision (CV), but their effect on network security apps rests in the field of open investigation. As IoT, AI, and 5G endure to unite and understand the potential of Industry 4.0, security events and incidents on IoT systems have been enlarged.
View Article and Find Full Text PDFNat Commun
January 2025
Digital Biomarkers for Oncology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany.
Accurate melanoma diagnosis is crucial for patient outcomes and reliability of AI diagnostic tools. We assess interrater variability among eight expert pathologists reviewing histopathological images and clinical metadata of 792 melanoma-suspicious lesions prospectively collected at eight German hospitals. Moreover, we provide access to the largest panel-validated dataset featuring dermoscopic and histopathological images with metadata.
View Article and Find Full Text PDFJ Hazard Mater
January 2025
School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan 430070, China; Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System, Wuhan 430070, China. Electronic address:
Artificial intelligence-assisted imaging biosensors have attracted increasing attention due to their flexibility, allowing for the digital image analysis and quantification of biomarkers. While deep learning methods have led to advancements in biomarker identification, the diversity in the density and adherence of targets still poses a serious challenge. In this regard, we propose CellNet, a neural network model specifically designed for detecting dense targets.
View Article and Find Full Text PDFPLOS Glob Public Health
January 2025
CEPED, IRD-Université de Paris, ERL INSERM SAGESUD, Paris, France.
Bangladesh completed a primary series of COVID-19 vaccinations for about 86 individuals per 100 population as of 5 July 2023. However, ensuring higher coverage in vulnerable areas is challenging. We report on the COVID-19 vaccine uptake and associated factors among adults in two vulnerable areas in Bangladesh.
View Article and Find Full Text PDFPLoS One
January 2025
School of Civil and Architectural Engineering, Harbin University, Harbin, China.
This work explores an intelligent field irrigation warning system based on the Enhanced Genetic Algorithm-Backpropagation Neural Network (EGA-BPNN) model in the context of smart agriculture. To achieve this, irrigation flow prediction in agricultural fields is chosen as the research topic. Firstly, the BPNN principles are studied, revealing issues such as sensitivity to initial values, susceptibility to local optima, and sample dependency.
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