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Involving community members in designing behavioural weight management programmes: a scoping review
Get PDFBackground Involving community members when developing health programmes can improve intervention outcomes. We undertook a scoping review to describe how community members contributed to the development of Behavioural Weight Management Programmes (BWMPs). Different terms have been used to describe this process, including co-design, co-production, Community-Based Participatory Research, or Patient and Public Involvement and Engagement. Our aim was to describe: (1) at what stage(s) communities were involved (e.g. planning, delivering and/or evaluating); (2) what level of involvement they had (e.g. leading, collaborating, consulted, informed or not involved); and (3) examples of how they were involved.
Methods We searched MEDLINE, EMBASE and CINAHL databases from 2010 to 2023. Two authors independently screened papers and extracted information using predefined criteria. We extracted data on study characteristics, and stages, levels and methods of community involvement.
Results We identified 58 BWMPs reported in 91 papers. Most were conducted in the US (n = 48, 83%). Their focus included race and ethnicity (n = 43, 73%), gender (n = 17, 29%) or low-income/underserved communities. Community members initiated the development of BWMPs in 36% of programmes (n = 21). Most programmes used community involvement to adapt an existing intervention (n = 33, 57%). Community involvement was highest at the planning stage where 55% (n = 32) of studies included community members as collaborators and 9% (n = 5) had community members leading the process. At the delivery stage, nine studies (16%) were led by community members and 19 (33%) included them as collaborators. In the evaluation stage, no studies were led by community members but a quarter (n = 14, 24%) included them as collaborators. Few programmes reported either the cost (n = 3, 5%) or the duration (n = 13, 22%) of community involvement. Programme adaptations ranged from relatively easy-to-implement changes such as changing language or menus, to more substantive adaptations like format, activity and personnel.
Conclusions Our review identified substantial levels of community involvement (leadership or collaboration) in planning BWMPs, but less so in their delivery, and rarely in evaluation. Greater involvement of communities in evaluation would ensure programmes focus on what matters most to them. Reporting of community involvement, especially costs and time involved, should be improved to allow for shared learning
Pre-Season Total Energy Expenditure and Dietary Intake of Professional Male Soccer Players: A Doubly Labelled Water Study
Get PDFLimited data exist describing how professional footballers meet their energy requirements during pre-season, a phase characterised by increased training volume and a progressive shift from general conditioning to football-specific preparation. This study quantified total, resting, and activity energy expenditure (AEE), diet-induced thermogenesis, water turnover, and dietary intake in six professional male soccer players (age: 25 ± 1 year; height: 182.5 ± 10.1 cm; body mass: 77.8 ± 8.2 kg). Players were studied across 14 consecutive days, representing training-only and training-plus-match microcycles. Total energy expenditure (TEE) was measured using doubly labelled water, resting energy expenditure (REE) by indirect calorimetry and dietary intake using the remote food photography method. Fourteen-day mean TEE, REE, AEE and water turnover were 13.25 ± 1.31 MJ⋅day−1, 7.96 ± 0.89 MJ⋅day−1, 4.20 ± 1.03 MJ⋅day−1, 5.16 ± 0.66 L⋅day−1, respectively. Physical activity level was 1.67 ± 0.16 AU. Energy, carbohydrate, protein, and fat intakes were 10.95 ± 1.52 MJ⋅day−1, 2.8 ± 0.6 g⋅kg−1⋅day−1, 2.2 ± 0.4 g⋅kg−1⋅day−1, and 1.5 ± 0.4 g⋅kg−1⋅day−1, respectively. Total energy expenditure was not significantly different between training-only and training-plus-match microcycles (+1.89 ± 1.98 MJ⋅day−1; ES = 0.95 ± 1.08; p = 0.100). No significant differences were observed in energy or macronutrient intake across weekly microcycles (p > 0.068) or between days (p > 0.144). Players did not achieve energy balance or align dietary intake with day-to-day training demands, suggesting limited nutrition periodisation during pre-season. These findings highlight the need for practitioners to implement strategies supporting fuelling, recovery and adaptation during this critical phase
A Data-Driven Approach to Identifying Cost-Effective Retrofits, Pre-dicting Energy Ratings, and Evaluating National Retrofit CO₂ Savings in UK Homes
No full textEnergy efficiency is an important factor contributing to the sustainability and for reducing energy costs. There has been an increasing attention in residential energy performance, but detailed studies exploring cost-effectiveness analysis, predictive modelling, and adoption modelling are still lacking. This study addresses these issues by analysing a large Energy Performance Certificate (EPC) dataset in the UK in 2024, having over 4.8 million property records. The research explores retrofit costs and impact data to investigate three critical research questions. First, we evaluated the energy efficiency and CO₂ savings per pound spent across different property types in the UK, analysing 41 retrofit improvement types using statistical analysis. Second, machine learning models were trained to predict a building's energy rating from its efficiency and structural traits. Third, standard retrofit interventions were assessed for defining the actual CO₂ savings by integrating retrofit adoption probabilities and Monte Carlo simu-lations. Our results show that the highest energy efficiency per pound spent could be achieved with inexpensive improvements like low-energy lighting, installing hot water cylinders and draught proofing. The Voting Classifier model (XGB + RF) achieved the best discrimination with 70.8% outperforming XGBoost (69.4%), Random Forest (69.09%), and MLP Neural Network (59.5%). The simulations based on different adoption scenarios demonstrate that even a small increase in the adoption rates can lead to significant national CO₂ reductions. Overall, this study provides a transferable methodology that combines cost-effectiveness analysis, predictive analysis, and retrofit adoption modelling for sustainable housing research in the UK. The findings offer insightful applicability to guide retrofit priority, policy targeting, and future studies in sustainable residential energy planning
“We know we are outsiders”: migrant experiences of antisocial behaviour victimisation
No full textBoth migration and antisocial behaviour (ASB, defined as behaviour that causes nuisance, annoyance, alarm or distress) are contentious issues, with the UK Labour government prioritising both since taking office in 2024 (Home Office, 2025; Sigona, 2025). Levels of migration and cultural diversity are often placed alongside debates of community cohesion (Collic-Peisker and Robertson, 2014). Additionally, ASB has been viewed as a potential threat to community cohesion, reducing levels of trust amongst local residents (Home Office, 2023). Politically, issues of cultural diversity, community cohesion and nuisance and distressing behaviour are often linked (Collic-Peisker and Robertson, 2014), however, despite this, there has been limited research linking these two issues. Therefore, this research aimed to explore migrant experiences of ASB. Qualitative interviews were conducted with people who had moved to the UK and people who work with asylum seekers, refugees and other migrant populations based in the Yorkshire and Humber region of England. The research found that participants regularly experienced ASB. Some of these behaviours did not appear to be targeted at migrant populations and were instead thought to be witnessed due to living in or visiting areas with high levels of socioeconomic issues which are also more likely to experience higher levels of ASB (Home Office, 2023). Nevertheless, participants reported many behaviours they felt were targeted at them due to intersecting issues of ethnicity, migration status, age and/or gender. Whilst ASB could come from a number of sources, including both children and adults living in the local area, it was also reported from services such as the police, welfare providers and housing providers, suggesting the organisations who are tasked with preventing and responding to ASB may also be perpetrators of distressing behaviour much of which seemed to be motivated by racist or xenophobic views. These findings help to highlight a significant gap in understandings of who are perpetrators and who are victims of ASB and demonstrates the importance of listening to migrant experiences when exploring issues of social integration and/or unrest
Learning Dynamics, Pattern Recognition Capability and Interpretability of the Tsetlin Machine
Get PDFThe inability to trace an AI’s reasoning process and understand why it makes each decision is known as the black box problem. This remains one of the major barriers to the trusted and widespread use of machine learning in many application domains. The paper explores pattern recognition performance and learning dynamics of the Tsetlin Machine – a new explainable logic-based machine-learning approach. Tsetlin Machine uses a collection of finite-state automata with a unique logic-based learning mechanism and provides a promising alternative to Artificial Neural Networks with several advantages, such as interpretability, low complexity, suitability for hardware implementation and high performance. This work investigates Tsetlin Machine’s mechanism for constructing conjunctive clauses from data and their interpretation for pattern recognition on several datasets. We demonstrate that during training the logical clauses learn persistent sub-patterns within the class. Each clause creates a class template by clustering a certain number of similar class samples, combining them through literal-wise logical conjunction (i.e., AND-ing). The number of class samples that each clause combines depends on Tsetlin Machine’s hyperparameters. The more class samples that are combined, the more general the clauses become. The paper aims at uncovering how Tsetlin Machine’s hyperparameters influence the balance between clause generalization and specialization and how this affects the accuracy of pattern recognition. It also studies the evolution of the machine’s internal state, its convergence and training completion
Artificial Intelligence-Based Prediction of Compressive Strength in High-Performance Eco-Friendly Concrete Incorporating Recycled Waste Glass
Get PDFThis study investigates the application of artificial intelligence for predicting the compressive strength of a high-performance, eco-efficient engineered cementitious composite (ECC), designated mix S8-1, A. The composite incorporates supplementary cementitious materials and alternative aggregates derived from recycled glass waste. The binder system combines waste glass powder and silica fume, while the aggregate fraction includes recycled cobalt glass. An extensive experimental program involving 14 mixtures tested at 7, 28, 56, 90, and 120 days was performed to establish the reference mechanical and rheological properties. Mix S8-1, A achieved strength class C60/75 and workability corresponding to consistency class S4. To substantiate long-term performance, microstructural and chemical analyses were conducted on specimens preserved since 2011, using scanning electron microscopy (SEM) and X-ray fluorescence (XRF). The results confirmed a stable, densified microstructure, evidencing the long-term durability of the patented ECC formulation. For predictive modeling, a shallow feedforward artificial neural network with three hidden layers was developed and trained on 70 dataset entries representing mixture proportions and curing ages. Model performance was evaluated using cross-validation, achieving a coefficient of determination (R2) of 0.968, a mean absolute error of 1.96 MPa, and a root mean square error of 2.52 MPa. The results demonstrate that AI-based approaches can accurately predict the compressive strength of high-performance, environmentally sustainable ECCs incorporating recycled glass constituents, supporting both performance optimization and resource-efficient material design
The systems evaluation network: building capability and capacity in the use of systems science across public health
Get PDFBackground:
The Systems Evaluation Network (SEN) aims to build capability and capacity regarding the use of systems science in public health evaluation. The SEN was established in June 2021 and 3 years from its inception, we undertook a member survey to understand the engagement with, and impact of, the SEN.
Methods:
An 18-item cross-sectional survey captured quantitative and qualitative responses regarding SEN member perspectives, centring around their experience of the SEN, associated impacts, and future requirements. We analysed quantitative data descriptively and qualitative data through content analysis. Sub-group analyses explored differences between those working in academia vs practice/policy.
Results:
Seventy-three participants completed the survey, with 60% working in academia and 40% in practice/policy. Considering experiences of the SEN, participants felt the SEN has shared information about innovative methods and evaluation approaches (94.0% agreed), has provided the opportunity to share and learn with other members (86.0% agreed), and has improved knowledge of systems evaluation methods (86.2% agreed). Regarding impacts of the SEN, participants stated that the SEN has increased their capability to apply systems-oriented methods and evaluation of systems approaches (76% agreed) and has facilitated relationships with others (56.9% agreed). Participants shared future capability requirements for evaluation, which focused on methods (e.g. systems dynamics modelling and ripple effects mapping), approaches (e.g. developmental evaluation and embedded researchers), and other ways in which capability could be increased (e.g. by using case studies).
Conclusion:
This paper illustrates the experiences and impacts of the SEN, identifying its strengths such as the wide range of topics/content and the flexible and accessible delivery format, but contrast against the difficulties of fostering new relationships in an online setting. These findings can help inform the future direction of the SEN and provide insight to other online communities of practice
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