Introduction: The current study builds on the expertise of National Gallery Singapore and Nanyang Technological University Singapore (NTU) in developing and piloting an enhanced version of the Slow Art program, namely "Slow Art Plus" for mental health promotion.
Methods: A single-site, open-label, waitlist Randomized Control Trial (RCT) design comprising of a treatment group and waitlist control group was adopted (ClinicalTrials.gov ID: NCT05803226). Participants ( = 196) completed three online questionnaires at three timepoints: baseline [T1], immediately post-intervention/s baseline [T2], post-intervention follow-up/immediately post-intervention [T3]. Qualitative focus groups were conducted to evaluate program acceptability.
Results: A mixed model ANOVA was performed to understand intervention effectiveness between the immediate intervention group and waitlist control group. The analyses revealed a significant interaction effect where intervention group participants reported an improvement in spiritual well-being ( = 0.001), describing their thoughts and experiences ( = 0.02), and nonreacting to inner experiences ( = 0.01) immediately after Slow Art Plus as compared to the control group. Additionally, one-way repeated measure ANOVAs were conducted for the intervention group to evaluate maintenance effects of the intervention. The analyses indicated significant improvements in perceived stress ( < 0.001), mindfulness (p < 0.001) as well as multiple mindfulness subscales, active engagement with the world ( = 0.003), and self-compassion ( = 0.02) 1 day after the completion of Slow Art Plus. Results from framework analysis of focus group data revealed a total of two themes (1: Experiences of Slow Art Plus, 2: Insights to Effective Implementation) and six subthemes (1a: Peaceful relaxation, 1b: Self-Compassion, 1c: Widened Perspective, 2a: Valuable Components, 2b: Execution Requisites, 2c: Suggested Enhancements), providing valuable insights to the overall experience and implementation of the intervention.
Discussion: Slow Art Plus represents a unique approach, offering a standardized, multimodal, single-session program that integrates mindfulness and self-compassion practices, as well as reflective and creative expressions with Southeast Asian art. It demonstrates potential in meeting the mental health needs of a wide range of individuals and could be readily incorporated into social prescribing initiatives for diverse populations.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11128664 | PMC |
| http://dx.doi.org/10.3389/fpubh.2024.1238564 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
A novel adaptive FOCV algorithm with robust IMRAC control for sustainable and high-efficiency MPPT in standalone PV systems: experimental validation and performance assessment.
Sci Rep
December 2024
Department of Electrical and Electronics, Faculty of Engineering, Alberoni University, Kohistan, Kapisa, Afghanistan.
This paper introduces an innovative, adaptive Fractional Open-Circuit Voltage (FOCV) algorithm combined with a robust Improved Model Reference Adaptive Controller (IMRAC) for Maximum Power Point Tracking (MPPT) in standalone photovoltaic (PV) systems. The proposed two-stage control strategy enhances energy efficiency, simplifies system operation, and addresses limitations in conventional MPPT methods, such as slow convergence, high oscillations, and susceptibility to environmental fluctuations. The first stage dynamically estimates the Maximum Power Point (MPP) voltage using a novel adaptive FOCV method, which eliminates the need for irradiance sensors or physical disconnection of PV modules.
View Article and Find Full Text PDFPath planning and engineering problems of 3D UAV based on adaptive coati optimization algorithm.
Sci Rep
December 2024
Key Laboratory of Advanced Manufacturing Technology, Ministry of Education, Guizhou University, Guiyang, 550025, Guizhou, China.
In response to the challenges faced by the Coati Optimization Algorithm (COA), including imbalance between exploration and exploitation, slow convergence speed, susceptibility to local optima, and low convergence accuracy, this paper introduces an enhanced variant termed the Adaptive Coati Optimization Algorithm (ACOA). ACOA achieves a balanced exploration-exploitation trade-off through refined exploration strategies and developmental methodologies. It integrates chaos mapping to enhance randomness and global search capabilities and incorporates a dynamic antagonistic learning approach employing random protons to mitigate premature convergence, thereby enhancing algorithmic robustness.
View Article and Find Full Text PDFElectrochemical and Optical Multi-Detection of Through Magneto-Optic Nanoparticles: A Pencil-on-Paper Biosensor.
Biosensors (Basel)
December 2024
Department of Electrical-Electronics Engineering, Abdullah Gul University, Kayseri 38039, Türkiye.
detection suffers from slow analysis time and high costs, along with the need for specificity. While state-of-the-art electrochemical biosensors are cost-efficient and easy to implement, their sensitivity and analysis time still require improvement. In this work, we present a paper-based electrochemical biosensor utilizing magnetic core-shell FeO@CdSe/ZnS quantum dots (MQDs) to achieve fast detection, low cost, and high sensitivity.
View Article and Find Full Text PDFDenseIncepS115: a novel network-level fusion framework for Alzheimer's disease prediction using MRI images.
Front Oncol
December 2024
ICT Convergence Research Centre, Soonchunhyang University, Asan, Republic of Korea.
Article Synopsis
- Alzheimer's disease (AD) is increasingly prevalent among individuals aged 65 and older, with early prediction crucial to slowing its progression due to the lack of effective treatments once symptoms appear.
- Researchers have developed a deep learning architecture called DenseIncepS115 that combines advanced modules with self-attention mechanisms to improve AD prediction accuracy from MRI scans.
- This architecture achieved impressive accuracy rates of 99.5% and 98.5% on two publicly available datasets, demonstrating superior performance compared to existing techniques.
A Deep Learning Network for Accurate Retinal Multidisease Diagnosis Using Multiview Fusion of En Face and B-Scan Images: A Multicenter Study.
Transl Vis Sci Technol
December 2024
Department of Ophthalmology, The Second People's Hospital of Foshan, Foshan, China.
Purpose: Accurate diagnosis of retinal disease based on optical coherence tomography (OCT) requires scrutiny of both B-scan and en face images. The aim of this study was to investigate the effectiveness of fusing en face and B-scan images for better diagnostic performance of deep learning models.
Methods: A multiview fusion network (MVFN) with a decision fusion module to integrate fast-axis and slow-axis B-scans and en face information was proposed and compared with five state-of-the-art methods: a model using B-scans, a model using en face imaging, a model using three-dimensional volume, and two other relevant methods.
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!