98%
921
2 minutes
20
There is currently a need to identify feasible and effective interventions to help older individuals suffering from memory loss maintain functional independence and quality of life. To improve upon paper and pencil memory notebook interventions, the Digital Memory Notebook (DMN) application (app) was developed iteratively with persons with cognitive impairment. In this paper we detail a manual-based intervention for training use of the DMN app. A series of three case studies are described to illustrate the clinical process of the DMN intervention, the key components of the intervention and participants' perceptions of the intervention. The Reliable Change Index was applied to pre/post intervention scores that examined everyday memory lapses, daily functioning, coping self-efficacy, satisfaction with life, and quality of life with standardized measures. Following the intervention, two of three participants self-reported a clinically significant reduction in everyday memory lapses and improved everyday functioning. One participant reported clinically significant change in quality of life. All participants demonstrated clinically significant changes in their ability to cope with problems and build self-efficacy. Furthermore, all participants scored in the normative range post-intervention on the measure of satisfaction with life. Clinical observations and participant feedback were used for refinement of the DMN intervention (ClinicalTrials.gov NCT03453554).
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6825881 | PMC |
http://dx.doi.org/10.1080/09602011.2019.1611606 | DOI Listing |
Behav Res Methods
August 2025
Department of Psychology, University of Arizona, Tucson, AZ, USA.
The autobiographical interview is a widely used tool for examining memory and related cognitive functions. It provides a standardized framework to differentiate between internal details, representing the episodic features of specific events, and external details, including semantic knowledge and other non-episodic information. This study introduces an automated scoring model for autobiographical memory and future thinking tasks, using large language models (LLMs) that can analyze personal event narratives without preprocessing.
View Article and Find Full Text PDFJ Chem Inf Model
July 2025
School of Chemistry, University of Nottingham, University Park, Nottingham NG7 2RD, U.K.
Computer-Assisted Synthesis Programs are increasingly employed by organic chemists. Often, these tools combine neural networks for policy prediction with heuristic search algorithms. We propose two novel enhancements, which we call eUCT and dUCT, to the Monte Carlo tree search (MCTS) algorithm.
View Article and Find Full Text PDFBMC Bioinformatics
June 2025
Key Laboratory of Qinghai-Tibetan Plateau Animal Genetic Resource Reservation and Utilization, Sichuan Province and Ministry of Education, Southwest Minzu University, Chengdu, 610225, China.
Background: Tandem repeats (TRs) are major sources of genetic variation and important genetic markers. Their expansions are not only involved in gene expression regulation but also associated with many nervous system diseases and cancers. However, there is a lack of an efficient tandem repeat identification tool for seamless integration with larger bioinformatics programs developed with the popular Python language.
View Article and Find Full Text PDFMem Cognit
August 2025
Department of Psychology, University of Waterloo, Waterloo, ON, Canada.
We often use tools and aids to help us achieve our cognitive goals - that is, we often offload to external supports. One such variety of offloading is the use of external memory stores (e.g.
View Article and Find Full Text PDFJ Chem Inf Model
December 2024
Key Laboratory of Biorheological Science and Technology (Chongqing University), Ministry of Education, Bioengineering College, Chongqing University, Chongqing 400044, China.
The classification of bioactive peptides is of great importance in protein biology, but there is still a lack of a universal and effective classifier. Inspired by video action recognition, we developed the UniBioPAN architecture to create a universal peptide classifier to solve this problem. The architecture treats the peptide sequence as a video sequence and the molecular image of each amino acid in the peptide sequence as a video frame, enabling feature extraction and classification using convolutional neural networks, bidirectional long short-term memory networks, and fully connected networks.
View Article and Find Full Text PDF