Post-concussion symptom burden and dynamics: Insights from a digital health intervention and machine learning

Individuals who sustain a concussion can experience a range of symptoms which can significantly impact their quality of life and functional outcome. This study aims to understand the nature and recovery trajectories of post-concussion symptomatology by applying an unsupervised machine learning approach to data captured from a digital health intervention (HeadOn). As part of the 35-day program, patients complete a daily symptom diary which rates 8 post-concussion symptoms. Symptom data were analysed using K-means clustering to categorize patients based on their symptom profiles. During the study period, a total of 758 symptom diaries were completed by 84 patients, equating to 6064 individual symptom ratings. Fatigue, sleep disturbance and difficulty concentrating were the most prevalent symptoms reported. A decline in symptom burden was observed over the 35-day period, with physical and emotional symptoms showing early rates of recovery. In a correlation matrix, there were strong positive correlations between low mood and irritability (r = 0.84), and poor memory and difficulty concentrating (r = 0.83). K-means cluster analysis identified three distinct patient clusters based on symptom severity. Cluster 0 (n = 24) had a low symptom burden profile across all the post-concussion symptoms. Cluster 1 (n = 35) had moderate symptom burden but with pronounced fatigue. Cluster 2 (n = 25) had a high symptom burden profile across all the post-concussion symptoms. Reflecting the severity of the clusters, there was a significant relationship between the symptom clusters for both the Rivermead (p = 0.05) and PHQ-9 (p = 0.003) questionnaires at 6-weeks follow-up. By leveraging digital ecological momentary assessments, a rich dataset of daily symptom ratings was captured allowing for the identification of symptom severity clusters. These findings underscore the potential of digital technology and machine learning to enhance our understanding of post-concussion symptomatology and offer a scalable solution to support patients with their recovery

Cost-effectiveness of swab versus tissue sampling for infected diabetic foot ulcers from the CODIFI2 randomised controlled trial

This multi-centre, Phase III, prospective, unblinded, two-arm parallel group, randomised controlled trial compared clinical (reported elsewhere) and economic outcomes of swab versus tissue sampling over a 52–104 week period. Resource use was logged using case record forms and patient questionnaire at weeks 4, 12, 26, 39, 52 and 104, costed using laboratory and published sources from the UK NHS perspective, at 2021/2022 price-year. EQ-5D-3L questionnaires issued at these time points were used to derive quality-adjusted life-years (QALYs). To account for imbalances such as age, a regression-based approach was used to estimate survival, expected costs and QALYs between the sampling arms. Available case analysis (ACA) and multiple imputation methods were applied for self reported missing data, and ACA for researcher-collected data (survival, hospitalisations and antibiotic use). Probabilistic sensitivity analysis was used to assess the uncertainty of economic results. Results We recruited 149 participants (75 swab, 74 tissue) from 21 UK sites, between 07 May 2019 and 28 April 2022 (last follow-up 28 April 2023). Planned sample size was 730 participants, for 90% power to detect 12.5% difference in healing at 52 weeks, but the trial stopped early due to low recruitment. Expected QALYs in the swab-sampling arm were greater than in the tissue-sampling arm at weeks 26, 52 and 104. The cost of tissue sampling was greater than of swabbing when including antibiotics and hospitalisation. Swab sampling participants had higher QALYs and lower costs across weeks 26–52, reducing slightly by week 104. Conclusions Because of higher costs, lower QALYs and lack of evidence of benefit, potentially due to the trial being underpowered, tissue sampling was dominated by wound swabbing in the cost-effectiveness analysis

QCA: Quantum Computational Approach for Internet of Things with 5G Connectivity

In this paper, the need for a quantum computing approach is analyzed for IoT applications using the 5G resource spectrum. Most of the IoT devices are connected for data transmission to end users with remote monitoring units, but there are no sufficient data storage units, and more data cannot be processed at minimized time periods. Hence, in the proposed method, quantum information processing protocols and quantum algorithms are integrated where data transmissions are maximized. Further, the system model is designed in such a way for checking the external influence factors that prevent the IoT device from transmitting data to end users. Therefore, with corresponding signal and noise power, it is essential to process the transmissions, thereby increasing data proportions at end connectivity. Once quantum computations are performed, then it is crucial to normalize IoT data units, thus establishing control over entire connected nodes that create a gateway for achieving maximum throughput. The combined system model is tested under four cases where the comparative outcomes prove that with reduced queue reductions of 12%, it is possible to achieve a maximum throughput of 99%

Elective peri‐operative management of adults taking glucagon‐like peptide‐1 receptor agonists, glucose‐dependent insulinotropic peptide agonists and sodium‐glucose cotransporter‐2 inhibitors: a multidisciplinary consensus statement

Summary Introduction Glucagon‐like peptide‐1 receptor agonists, dual glucose‐dependent insulinotropic peptide receptor agonists and sodium‐glucose cotransporter‐2 inhibitors are used increasingly in patients receiving peri‐operative care. These drugs may be associated with risks of peri‐operative pulmonary aspiration or euglycaemic ketoacidosis. We produced a consensus statement for the peri‐operative management of adults taking these drugs. Methods This multidisciplinary consensus statement included surgeons, anaesthetists, physicians, pharmacists and people with lived experience relevant to these guidelines. Following the directed literature review, a three‐round modified Delphi process was conducted to generate and ratify recommendations. Results Patients taking glucagon‐like peptide‐1 receptor agonists and dual glucose‐dependent insulinotropic peptide receptor agonists should: continue these drugs before surgery; have full risk assessment and stratification; and receive peri‐operative techniques that may mitigate risk of pulmonary aspiration before, during and after sedation or general anaesthesia. Patients taking sodium‐glucose cotransporter‐2 inhibitors should omit them the day before and the day of a procedure. All patients should have risks and mitigation strategies discussed with a shared decision‐making approach. Discussion Until more evidence becomes available, this pragmatic, multidisciplinary consensus statement aims to support shared decision‐making and improve safety for patients taking glucagon‐like peptide‐1 receptor agonists, dual glucose‐dependent insulinotropic peptide receptor agonists and sodium‐glucose cotransporter‐2 inhibitors during the peri‐operative period

‘Why don’t you…’: R.J. Morris and the European Association for Urban History

Bob Morris was elected as president of the European Association for Urban History (EAUH) ahead of its 2002 conference in Edinburgh. Bob’s presidency, and the Edinburgh conference specifically, took place at an important point in the development of urban history within Europe and further afield. First, the programme reveals several emerging themes and topics of interest that have since shaped the sub-field in new and innovative ways. Second, Bob’s informal and collegial approach towards networking is reflected in the decision to place the EAUH on a quasi-formal constitutional basis. Both of these developments reflect, in part, Bob’s own research interests, as well as the sub-field’s welcoming approach to younger researchers, including taught and research postgraduate students, interested in networking with more established scholars

“We go hunting …”: Understanding experiences of people living with obesity and food insecurity when shopping for food in the supermarket to meet their weight related goals

The high prevalence of food insecurity in the United Kingdom has been exacerbated by the cost-of-living crisis. In high-income countries, those experiencing food insecurity struggle to buy and consume foods that meet Government healthy eating recommendations, and are at increased risk of obesity, linked to poor diet quality. Individuals in high-income countries purchase most of their food to consume at home from supermarkets, making this an important context within which healthier and environmentally sustainable food purchasing should be supported. However, the lived experience of supermarket food purchasing in people living with obesity and food insecurity has not been explored in depth. Adults, living in England and Scotland, who self-identified as living with obesity and food insecurity and looking to reduce their weight, were recruited to take part in semi-structured interviews (n = 25) or focus groups (n = 8) to explore their experience of shopping for food in the supermarket. Using thematic analysis, four main themes were generated: 1) the Restricted Consumer; restrictions around the type of food purchased, where food can be purchased and the resulting emotional toll, 2) the Conscious Consumer; decision making and effortful practices both in preparation of and during the shopping trip, 3) Mitigating the Rising Cost of Food; agency and actions taken to mitigate high food prices, 4) Stigma; instances of perceived and/or experienced weight and poverty-related stigma and the physical actions and cognitive social comparisons used to minimise stigma. Findings provide insights for evidence-based policy on the need for upstream changes within the wider food system to address the social determinants of health and support people living with obesity and food insecurity to eat healthier and more sustainable diets

Meaning in life research: the importance of considering auxiliary assumptions

There have been significant advances in the science of meaning in life (MIL). Researchers have made empirical predictions about the antecedents and consequences of meaning and the best ways it can be enhanced. Yet, it is important that researchers in this area consider the auxiliary assumptions associated with their predictions. Auxiliary assumptions, which traverse the distance from nonobservational theoretical terms to observational terms at the level of the empirical hypotheses, have important implications for the appraisal of empirical victories and defeats. In this paper, we outline the importance of auxiliary assumptions in MIL research. To ensure the validity of findings associated with MIL, we hope this paper encourages researchers to pay close attention to the auxiliary assumptions associated with their predictions

Impact of Patients Decision Aids on Shared Decision‐Making and Patient Satisfaction Prior to Pelvic Floor Surgery

Objective Evaluate usability and utility of National Institute for Clinical Excellence (NICE) Patient Decision Aid's (PtDA's) for pelvic floor surgery. PtDA's reviewed were uterine prolapse, vault prolapse and stress urinary incontinence (SUI). Design Ten women given the PtDA's during routine clinical care were recruited from each cohort and underwent a semi‐structured interview with a clinical researcher. Setting Urogynaecology outpatients in an NHS tertiary teaching hospital. Population or Sample Women considering surgical management of uterine/vault prolapse or SUI. Exclusion criteria included those under 18, unable to communicate in English or not eligible for all surgical options discussed in the PtDA's. Methods A qualitative, semi‐structured interview evaluating women's opinions of the decision aid and the way in which they utilised the PtDA was conducted. The interviews were recorded and transcribed prior to undertaking thematic analysis utilising NVivo software. Main Outcome Measure The outcomes of interest were feedback for content, language, format and usage of the PtDA's and women's usage of PtDA's in decision‐making. Results Amendments suggested included removal of mesh from the SUI PtDA as this is not routinely available on the NHS and addition of a statement regarding the use of mesh in prolapse surgery. Additional anatomical diagrams were suggested. The need for a robust and regular update system was highlighted as was the provision of foreign language, audio and electronic versions. Conclusions PtDA's need updating to ensure relevant content. Clear, detailed and relevant information is required alongside active clinician engagement to reach a mutually agreeable treatment plan

The Relationship Between Tensor Fascia Latae and Gluteus Maximus Has the Potential to Indicate Early Intra-articular and Degenerative Pathologies of the Femoral-Acetabular Joint: A Narrative Review.

Intra-articular and degenerative hip pathologies have become common place with the number of total hip replacements rising year on year in the United Kingdom (UK). Pathology is identified by clinicians using special tests which are researched maneuvers used by clinicians to rule in or rule out specific musculoskeletal pathologies. Special tests used for hip pathology usually have high specificity to exclude degenerative and intraarticular pathology but vary in sensitivity. These special tests are usually only conducted when a person is symptomatic and typically require radiological confirmation to diagnose. The aim of this review was to appraise research to determine whether functional changes in the TFL and UGM muscle complex could indicate degenerative and/or intra articular pathology, with a specific focus on the utility of the ratio in strength of TFL and UGM to assist clinical diagnosis. The hypothesis was that the ratio of the strength of Tensor Fascia Latae (TFL) and the upper fibres of Gluteus Maximus (UGM) could suggest early intra-articular hip pathology, and that changes to this ratio could indicate deterioration of the hip joint before symptoms present/progress. Level of Evidence

Sustainable Cold Chain Management: An Evaluation of Predictive Waste Management Models

The integration of advanced predictive models is pivotal for optimizing demand forecasting and inventory management in cold chain logistics. This study evaluates the application of machine learning techniques—ARIMA (Auto-Regressive Integrated Moving Average) and Multiple Linear Regression (MLR)—to forecast demand trends and analyze key drivers in a mid-sized cold chain operation. Trained on a multi-year sales dataset, the ARIMA model excelled in capturing seasonal patterns, while the MLR model effectively incorporated multivariable factors such as temperature, product type, and promotional activity. Both models demonstrated strong predictive accuracy, with low Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), offering reliable and computationally efficient solutions for mid-sized operations. The findings underscore the novelty of combining ARIMA’s time-series capabilities with MLR’s multivariable analysis to address complex demand drivers. By aligning with Resource-Based View (RBV) and Supply Chain Resilience Theory, this research advances the understanding of AI-driven predictive models as strategic tools for enhancing operational efficiency, reducing waste, and promoting sustainability in cold chain logistics. This work sets the stage for future innovations in AI-driven supply chain optimization