What influences patient decisions when selecting an obesity treatment?

Objective: The objectives of this study were to understand patient preferences for obesity treatments, to describe how patients choose treatment options, and what factors influence their decisions. Methods: This participatory action research used purposeful sampling to recruit 10 patients with complications of obesity. Photovoice was used as the qualitative research methodology. Recruitment took place in specialist clinics for metabolic dysfunction-associated steatotic liver disease, diabetes mellitus, hypertension, and chronic kidney disease. Two males and eight females aged 18-75 years, with a BMI greater than 35 kg/m2 were recruited. Participants watched a 60-min â€‹video explaining nutritional, pharmacological, and surgical therapies in equipoise. Data was collected using photographs with a disposal camera followed by one-to-one semi-structured interviews. Afterward, this analysis utilised reflective thematic analysis. Results: Five main themes were identified that influenced patients' decisions when selecting an obesity treatment: 1] Accessibility issues, 2] Polypharmacy, 3] Fears around future health 4] Lack of Support 5] Information Mismanagement. Conclusion: The themes identified in this study represent the patients' voices for those living with obesity complications and what influences their decisions on treatment options. The findings underscore the need for a holistic and patient-centred approach to the management of obesity and its associated complications. Patient-centred care including knowledge, health literacy, support, and participation is essential to providing effective care for patients with obesity to make decisions between treatment options.

Use of telemedicine in general practice in Europe since the COVID-19 pandemic: A scoping review of patient and practitioner perspectives.

General practice is generally the first point of contact for patients presenting with COVID-19. Since the start of the COVID-19 pandemic general practitioners (GPs) across Europe have had to adopt to using telemedicine consultations in order to minimise the number of social contacts made. GPs had to balance two needs: preventing the spread of COVID-19, while providing their patients with regular care for other health issues. The aim of this study was to conduct a scoping review of the literature examining the use of telemedicine for delivering routine general practice care since the start of the pandemic from the perspectives of patients and practitioners. The six-stage framework developed by Arksey and O'Malley, with recommendations by Levac et al was used to review the existing literature. The study selection process was conducted according to the PRISMA Extension for Scoping Reviews guidelines. Braun and Clarke's' Thematic Analysis' approach was used to interpret data. A total of eighteen studies across nine countries were included in the review. Thirteen studies explored the practitioner perspective of the use of telemedicine in general practice since the COVID-19 pandemic, while five studies looked at the patient perspective. The types of studies included were: qualitative studies, literature reviews, a systematic review, observational studies, quantitative studies, Critical incident technique study, and surveys employing both closed and open styled questions. Key themes identified related to the patient/ practitioner experience and knowledge of using telemedicine, patient/ practitioner levels of satisfaction, GP collaboration, nature of workload, and suitability of consultations for telemedicine. The nature of general practice was radically changed during the COVID-19 pandemic. Certain patient groups and areas of clinical and administrative work were identified as having performed well, if not better, by using telemedicine. Our findings suggest a level of acceptability and satisfaction of telemedicine by GPs and patients during the pandemic; however, further research is warranted in this area.

MACGT: multi-dimensional automated clustering genotyping tool for analysis of microarray-based mini-sequencing data.

SUMMARY: Multi-dimensional Automated Clustering Genotyping Tool (MACGT) is a Java application that clusters complex multi-dimensional vector data derived from single nucleotide polymorphism (SNP) genotyping experiments using mini-sequencing based microarray chemistries such as arrayed primer extension (APEX). Spot intensity output files from microarray experiments across multiple samples are imported into MACGT. The datasets can include four channels of intensity data for each spot, replica spots for each SNP probe and multiple probe types (APEX and allele-specific APEX probes) on both DNA strands for each SNP. MACGT automatically clusters these multi-dimensionality datasets for each SNP across multiple samples. Incorporation of additional array datasets from known samples that have previously validated SNP genotype calls allows unknown samples to be automatically assigned a genotype based on the clustering, along with numerical measures of confidence for each genotype call. Calling accuracy by MACGT exceeds 98% when applied to genotyping data from APEX microarrays, and can be increased to >99.5% by applying thresholds to the confidence measures.