"Is it time to throw out the weighing scales?" Implicit weight bias among healthcare professionals working in bariatric surgery services and their attitude towards non-weight focused approaches.

Background: People living with overweight or obesity (PLwO) can be stigmatised by healthcare professionals (HCPs). Reducing focus on weight is a proposed strategy to provide less threatening healthcare experiences. Given the lack of research on weight bias within obesity services, this study aimed to explore implicit bias among obesity specialist HCPs and explore views on non-weight focused approaches. Methods: Obesity specialist HCPs were invited to a webinar, "An exploration of non-weight focused approaches within bariatric services", held in October 2021. Implicit weight bias was examined using the BiasProof mobile device test, based on the Implicit Association Test. Poll data was analysed descriptively, and qualitative data was analysed using framework analysis. Findings: 82 of the 113 HCPs who attended the webinar consented to contribute data to the study. Over half (51%) had an implicit weight bias against PLwO. Most (90%) agreed/strongly agreed that obesity services are too weight focused and that patients should not be weighed at every appointment (86%). Perceived benefits of taking a non-weight focused approach included patient-led care, reducing stigma and supporting patient wellbeing, while perceived barriers included loss of objectivity, inducing risk and difficulty demonstrating effectiveness. Interpretation: Our findings indicate that half of obesity specialists HCPs in our sample of 82 providers, who are primarily dieticians and psychologists, have an implicit weight bias against PLwO. HCPs feel that a weight-focused approach within services was a barrier to patient care, but that there is a lack of alternative non-weight focused measures. Further research is needed into substitute outcome measures for clinical practice, also seeking the views of PLwO, and into interventions to address implicit weight bias. Funding: Johnson & Johnson funded the BiasProof licence and publication open access charge.

Use of Artificial Intelligence to understand adults' thoughts and behaviours relating to COVID-19.

AIMS: The outbreak of severe acute respiratory syndrome coronavirus 2 (COVID-19) is a global pandemic that has had substantial impact across societies. An attempt to reduce infection and spread of the disease, for most nations, has led to a lockdown period, where people's movement has been restricted resulting in a consequential impact on employment, lifestyle behaviours and wellbeing. As such, this study aimed to explore adults' thoughts and behaviours in response to the outbreak and resulting lockdown measures. METHODS: Using an online survey, 1126 adults responded to invitations to participate in the study. Participants, all aged 18 years or older, were recruited using social media, email distribution lists, website advertisement and word of mouth. Sentiment and personality features extracted from free-text responses using Artificial Intelligence methods were used to cluster participants. RESULTS: Findings demonstrated that there was varied knowledge of the symptoms of COVID-19 and high concern about infection, severe illness and death, spread to others, the impact on the health service and on the economy. Higher concerns about infection, illness and death were reported by people identified at high risk of severe illness from COVID-19. Behavioural clusters, identified using Artificial Intelligence methods, differed significantly in sentiment and personality traits, as well as concerns about COVID-19, actions, lifestyle behaviours and wellbeing during the COVID-19 lockdown. CONCLUSIONS: This time-sensitive study provides important insights into adults' perceptions and behaviours in response to the COVID-19 pandemic and associated lockdown. The use of Artificial Intelligence has identified that there are two behavioural clusters that can predict people's responses during the COVID-19 pandemic, which goes beyond simple demographic groupings. Considering these insights may improve the effectiveness of communication, actions to reduce the direct and indirect impact of the COVID-19 pandemic and to support community recovery.

The use of consumer depth cameras for 3D surface imaging of people with obesity: A feasibility study.

OBJECTIVE: Three dimensional (3D) surface imaging is a viable alternative to traditional body morphology measures, but the feasibility of using this technique with people with obesity has not been fully established. Therefore, the aim of this study was to investigate the validity, repeatability and acceptability of a consumer depth camera 3D surface imaging system in imaging people with obesity. METHODS: The concurrent validity of the depth camera based system was investigated by comparing measures of mid-trunk volume to a gold-standard. The repeatability and acceptability of the depth camera system was assessed in people with obesity at a clinic. RESULTS: There was evidence of a fixed systematic difference between the depth camera system and the gold standard but excellent correlation between volume estimates (r2=0.997), with little evidence of proportional bias. The depth camera system was highly repeatable - low typical error (0.192L), high intraclass correlation coefficient (>0.999) and low technical error of measurement (0.64%). Depth camera based 3D surface imaging was also acceptable to people with obesity. CONCLUSION: It is feasible (valid, repeatable and acceptable) to use a low cost, flexible 3D surface imaging system to monitor the body size and shape of people with obesity in a clinical setting.