Purpose: This study explored perceptions of meaningful weight-loss and the level of change on two patient-reported outcome (PRO) measures, the 36-item Short Form Health Survey® [SF-36v2®] and Impact of Weight on Quality of Life Lite-Clinical Trials [IWQOL-Lite-CT], that individuals living with overweight or obesity consider to be meaningful and indicative of treatment success.
Methods: Thirty-three qualitative interviews were conducted in the US with adults living with overweight or obesity. Concept elicitation explored perceptions of minimally important/meaningful weight-loss using open-ended questions. Cognitive debriefing was used to understand thresholds for meaningful change on both measures.
Results: Most participants (n = 23/33) expected a 5% total body weight-loss to yield some benefit in physical functioning, while all participants expected a 10% weight-loss to provide a meaningful and noticeable improvement in their physical functioning. Participants indicated that an item-level 1-point score change on each measure would represent a noticeable improvement in physical functioning and indicate treatment success.
Conclusions: Participants expected moderate weight-losses to be noticeable, with ≥ 10% weight-loss yielding the most consistent results. The findings suggested that both measures provide strong opportunity to demonstrate treatment benefit in relation to physical functioning as a small change on the response scale would represent a noticeable improvement in participants' daily lives.
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http://dx.doi.org/10.1007/s11136-022-03191-2 | DOI Listing |
Chem Rev
January 2025
Department of Materials Science and Engineering, Northwestern University, Evanston, Illinois 60208, United States.
Recent breakthroughs in brain-inspired computing promise to address a wide range of problems from security to healthcare. However, the current strategy of implementing artificial intelligence algorithms using conventional silicon hardware is leading to unsustainable energy consumption. Neuromorphic hardware based on electronic devices mimicking biological systems is emerging as a low-energy alternative, although further progress requires materials that can mimic biological function while maintaining scalability and speed.
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January 2025
Clinic for Thoracic and Cardiovascular Surgery, Herz- und Diabeteszentrum NRW, Bad Oeynhausen, Germany.
JAMA Dermatol
January 2025
Department of Dermatology, University of Pennsylvania, Philadelphia.
Importance: Cutaneous chronic graft-vs-host disease (GVHD) is independently associated with morbidity and mortality after allogeneic hematopoietic cell transplant. However, the health-related quality-of-life (HRQOL) domains that are most important to patients are poorly understood.
Objective: To perform a concept elicitation study to define HRQOL in cutaneous chronic GVHD from the patient perspective and to compare experiences of patients with epidermal vs sclerotic disease.
ACS Chem Biol
January 2025
Institute of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acid Chemistry and Nanomedicine, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China.
As an important receptor in a host's immune and metabolic systems, NOD1 is usually activated by Gram-negative bacteria having -diaminopimelic acid (-DAP) in their peptidoglycan (PGN). But some atypical Gram-positive bacteria also contain -DAP in their PGN, giving them the potential to activate NOD1. The prevalence of -DAP-type Gram-positive bacteria in the gut, however, remains largely unknown.
View Article and Find Full Text PDFMethods Mol Biol
January 2025
Ecole Polytechnique Fédérale de Lausanne, School of Life Sciences, Institute of Bioengineering, Lausanne, Switzerland.
Gene expression memory-based lineage inference (GEMLI) is a computational tool allowing to predict cell lineages solely from single-cell RNA-sequencing (scRNA-seq) datasets and is publicly available as an R package on GitHub. GEMLI is based on the occurrence of gene expression memory, i.e.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!