In recent years, computerized and non-computerized cognitive remediation programs have been designed for both individual and group settings. We believe, however, that a common misconception lies in considering the efficiency of a cognitive remediation therapy as resulting from the sole use of a computer. This omits that metacognitive skills need also to be trained throughout the remediation phase. RECOS is a theory-based therapeutic approach designed to promote the transfer of cognitive skills to functional improvements. It involves working with one person at a time using both paper/pencil tasks and a set of interactive computer exercises. Paper/pencil exercises are used to promote problem-solving techniques and to help patients to find appropriate suitable strategies. During the following computerized 1-h session, therapists guide participants to the procedural dimension of the action, which refers to knowledge about doing things and relies on retrospective introspection. We assume that each patient has a rich and underestimated procedural knowledge he/she is not aware of. By providing complex and interactive environments, computerized exercises are recommended to bring this knowledge to light. When strategies used by the participant become conscious, conditional knowledge determines when and why to use them in real-life situations.
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http://dx.doi.org/10.3389/fpsyt.2016.00056 | DOI Listing |
Front Nutr
January 2025
School of Nursing, Fujian Medical University, Fuzhou, China.
Objective: In this study, our objective was to provide practice recommendations by thoroughly examining lifestyle interventions for adults diagnosed with metabolic dysfunction-associated steatotic liver disease (MASLD). This was achieved through a systematic review of the literature, specifically focusing on lifestyle modification interventions in adults with MASLD.
Methods: The PIPOST (Population, Intervention, Professional, Outcome, Setting, and Type of evidence) framework was used to identify the questions for summarizing evidence.
BMJ Support Palliat Care
December 2024
Université de Franche-Comté, UMR 1098, Besancon, France.
Background: Although the benefit of supportive care in the postcancer period is now well demonstrated, its implementation in the patient journey remains challenging. This article describes the development, since 2015 and in routine care, of supportive postcancer care comprising a multidisciplinary rehabilitation programme (MRP) based on exercise for patients with early breast cancer.
Methods: As part of quality control, we reviewed all patient files since the programme was implemented.
J Orthop Surg Res
December 2024
Teaching and Research Section of Clinical Nursing, Xiangya Hospital of Central South University, 87 Xiangya Road, Kaifu District, Changsha, Hunan Province, China.
Objectives: This study aims to identify predictors of knee osteoarthritis (KOA) risk in middle-aged population, construct and validate a nomogram for KOA in this demographic.
Methods: From June to December 2020, we conducted a cross-sectional survey on 5,527 middle-aged individuals from Changsha and Zhangjiajie cities in Hunan Province, selected using a stratified multi-stage random sampling method. Data collection involved a structured questionnaire encompassing general demographic, physical condition, and lifestyle behaviors dimensions.
Front Immunol
December 2024
Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom.
Background: Adults classified as immunosuppressed have been disproportionately affected by the COVID-19 pandemic. Compared to the immunocompetent, certain patients are at increased risk of suboptimal vaccine response and adverse health outcomes if infected. However, there has been insufficient work to pinpoint where these risks concentrate within the immunosuppressed spectrum; surveillance efforts typically treat the immunosuppressed as a single entity, leading to wide confidence intervals.
View Article and Find Full Text PDFEur J Vasc Endovasc Surg
December 2024
Department of Vascular Surgery, James Cook University Hospital, Middlesbrough, UK. Electronic address:
Objective: The decision to electively repair an abdominal aortic aneurysm (AAA) involves balancing the risk of rupture, peri-procedural death, and life expectancy. Random forest classifiers (RFCs) are powerful machine learning algorithms. The aim of this study was to construct and validate a random forest machine learning tool to predict two year survival following elective AAA repair.
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