'Everything I enjoy doing I just couldn't do': Biographical disruption for sport-related injury

Navigating vaccination choices: The intersecting dynamics of institutional trust, belonging and message perception among Congolese migrants in London, UK (a reflexive thematic analysis)

The COVID-19 pandemic disproportionately impacted intersectionally marginalised migrants, revealing systemic disparities in health outcomes and vaccine uptake. Understanding the underlying social and structural factors influencing health behaviours is necessary to develop tailored interventions for migrants, but these factors have been seldom explored. This qualitative study aimed to explore contextual factors shaping COVID-19 vaccination decision-making among Congolese migrants in the UK.A community-based participatory research study was designed and led by a community-academic partnership in London, UK (2021-2022). Peer-led, semi-structured interviews were conducted in Lingala with 32 adult Congolese migrants and explored beliefs, perceptions and lived experiences of migration, healthcare, vaccination and the COVID-19 pandemic. Reflexive thematic analysis generated two themes and a model conceptualising the vaccination decision-making process. Participants and community partners were financially compensated; ethics was granted by the University of London ethics committee (REC: 2021.0128).Participants highlighted the incompatibility of lockdown restrictions with their communal culture, which intensified feelings of exclusion and alienation. Concerns about COVID-19 vaccination were attributed to safety and effectiveness, partly informed by experiences and legacies of racial discrimination and exploitation. Inequality in the pandemic response and COVID-19 outcomes heightened participants' sense that their views and needs were being overlooked, and government sources and information were perceived as coercive. Our model depicts the interplay between institutional trust, belonging, and message perception, which shaped participants' vaccination decisions and led to (non-)engagement with COVID-19 vaccination. This research enhances understanding of how social and contextual factors may influence migrants' engagement with health interventions. It underscores the importance of partnering with migrant communities to understand their needs in context and co-design tailored interventions and inclusive messaging strategies that promote trust and belonging. Implementing systemic changes to address structural inequalities will be crucial to create an environment that supports engagement with health-protective behaviours and enhances health outcomes among migrant communities.

Health challenges and acute sports injuries restrict weightlifting training of older athletes

Objectives

To quantify acute injuries sustained during weightlifting that result in training restrictions and identify potential risk factors or preventative factors in Master athletes and to evaluate potentially complex interactions of age, sex, health-related and training-related predictors of injuries with machine learning (ML) algorithms.

Methods

A total of 976 Masters weightlifters from Australia, Canada, Europe and the USA, ages 35–88 (51.1% women), completed an online survey that included questions on weightlifting injuries, chronic diseases, sport history and training practices. Ensembles of ML algorithms were used to identify factors associated with acute weightlifting injuries and performance of the prediction models was evaluated. In addition, a subgroup of variables selected by six experts were entered into a logistic regression model to estimate the likelihood of an injury.

Results

The accuracy of ML models predicting injuries ranged from 0.727 to 0.876 for back, hips, knees and wrists, but were less accurate (0.644) for shoulder injuries. Male Master athletes had a higher prevalence of weightlifting injuries than female Master athletes, ranging from 12% to 42%. Chronic inflammation or osteoarthritis were common among both men and women. This was associated with an increase in acute injuries.

Conclusions

Training-specific variables, such as choices of training programmes or nutrition programmes, may aid in preventing acute injuries. ML models can identify potential risk factors or preventative measures for sport injuries.