Open Research Europe

Military aviation training noise remains understudied despite its widespread impacts across urban, rural, and wilderness areas. The predominance of low-frequency noise and repetitive training can create pervasive noise pollution, yet past research often fails to capture the full range of health and quality-of-life effects. This study analyzed two complaint datasets related to Whidbey Island Naval Air Station noise: U.S. Navy records (2017-2020) and Quiet Skies Over San Juan County data (2021-2023). We analyzed and mapped sentiment intensity from noise complaints relative to modeled annual noise exposure, developed a typology to classify impacts, and modeled the environmental and operational factors influencing complaints. Findings revealed widespread negative sentiment and anger, often beyond the bounds of estimated noise contours, suggesting that annual cumulative noise models inadequately estimate community impacts. Complaints consistently highlighted sleep disturbance, hearing and health concerns, and compromised home environments due to shaking, vibration, and disruption of daily life. Residents also reported significant social, recreational, and work disruptions, along with feelings of fear, helplessness, and concern for children's well-being. The number of complaints were strongly associated with training schedules, with late-night sessions being the strongest predictor. A delayed response pattern suggests residents reach a frustration threshold before filing complaints. Overall, our findings demonstrate persistent negative sentiment and diverse impacts from military aviation noise. Results highlight the need for improved noise metrics, modeling and operational adjustments to mitigate the most disruptive effects.

Military aviation training noise remains understudied despite its widespread impacts across urban, rural, and wilderness areas. The predominance of low-frequency noise and repetitive training can create pervasive noise pollution, yet past research often fails to capture the full range of health and quality-of-life effects. This study analyzed two complaint datasets related to Whidbey Island Naval Air Station noise: U.S. Navy records (2017-2020) and Quiet Skies Over San Juan County data (2021-2023). We analyzed and mapped sentiment intensity from noise complaints relative to modeled annual noise exposure, developed a typology to classify impacts, and modeled the environmental and operational factors influencing complaints. Findings revealed widespread negative sentiment and anger, often beyond the bounds of estimated noise contours, suggesting that annual cumulative noise models inadequately estimate community impacts. Complaints consistently highlighted sleep disturbance, hearing and health concerns, and compromised home environments due to shaking, vibration, and disruption of daily life. Residents also reported significant social, recreational, and work disruptions, along with feelings of fear, helplessness, and concern for children's well-being. The number of complaints were strongly associated with training schedules, with late-night sessions being the strongest predictor. A delayed response pattern suggests residents reach a frustration threshold before filing complaints. Overall, our findings demonstrate persistent negative sentiment and diverse impacts from military aviation noise. Results highlight the need for improved noise metrics, modeling and operational adjustments to mitigate the most disruptive effects.

Purpose: Alpha-gal syndrome (AGS) is an emerging health issue. This syndrome, caused by the bites of ticks, induces allergic reactions to the sugar molecule galactose-alpha-1,3-galactose after exposure to non-primate mammalian meat and other byproducts. Agricultural workers spend significant time outdoors placing them at an increased risk for tick bites and tick-borne diseases, like AGS. This study aimed to characterize farmers and ranchers' prior knowledge, symptomology, and diagnostic experiences with AGS. Methods: We conducted a cross-sectional survey of more than 200 farmers and ranchers with a self-reported AGS diagnosis. The survey captured farmers and ranchers' experiences related to prior knowledge and experience with tick bites and AGS, reported symptoms, and obtaining a diagnosis. Findings: A total of 201 respondents across 26 states participated in the survey, with the majority from Missouri and Oklahoma. We identified four distinct symptom clusters, with the most reported symptoms being abdominal cramping, diarrhea, itchy skin, and nausea. Women more often reported gastrointestinal discomfort, and men were more likely to be in the mild symptom category. On average, participants reported 2.98 medical provider visits before receiving a diagnosis, most being diagnosed by general practitioners and allergists. Conclusions: No previous studies have focused on the symptom and diagnostic experiences of farmers and ranchers with AGS. Capturing such data is essential as these workers may experience unique occupational challenges following AGS diagnosis. The diagnostic experience data support a continuing need to educate and empower AGS patients and providers, especially agricultural workers and providers serving rural communities.

Loneliness--the perceived discrepancy between desired and actual social connection--is a common and aversive psychological state associated with a range of adverse health outcomes. Several theoretical models suggest that these associations may operate partly through health behaviors. In this preregistered study, we used data from the All of Us Research Program to evaluate associations of loneliness and functional rurality (FR), a study-specific contextual index of reduced neighborhood accessibility, with Fitbit-derived physical activity and sleep outcomes. Final samples included 16,912 participants for physical activity analyses and 13,937 for sleep analyses. In adjusted models, higher FR was associated with greater loneliness ({beta} = 0.061, 95% CI [0.045, 0.077], p = 9.63 x 10-14). FR and loneliness were independently associated with fewer daily steps and lower moderate-to-vigorous physical activity. Loneliness was also associated with shorter sleep duration, greater sleep duration variability, higher odds of short sleep, and higher odds of low sleep efficiency. FR was not associated with sleep duration or sleep duration variability but showed a small positive association with mean sleep efficiency and lower odds of low sleep efficiency. Interaction analyses provided little evidence that FR modified the associations of loneliness with most outcomes, although the FR x loneliness interaction was significant for sleep duration variability, indicating that loneliness was more strongly associated with irregular sleep duration in higher-FR contexts. Sensitivity analyses using stricter valid-day thresholds, winsorization, quartile-based exposure coding, and a backward 30-day window yielded directionally similar findings. These results suggest that FR and loneliness are independently associated with lower physical activity, whereas loneliness shows a more consistent relationship with adverse sleep patterns.

Patients who participate in intracranial neuroscience research make invaluable contributions to our understanding of the brain, accelerating the development of neurotechnological interventions. Engagement of patients as part of this research presents unique challenges, where study goals can be distant from immediate clinical applications and require specialized domain knowledge. Yet methods for meaningfully integrating patient communities as part of these research efforts is essential, as intracranial neuroscience guides the application of artificial intelligence for understanding and enhancing human cognition. In order to identify what patients consider meaningful research engagement we interviewed individuals who participated in a study during their Deep Brain Stimulation (DBS) surgery and attended a group event where they interacted with our research team. Analysis of semi-structured interviews identified four main themes: interest in science and the future of clinical care, contributing to science to improve lives, connecting with others, and accessibility considerations. Based on these insights, we propose strategies for transformational participation of patient communities in intracranial neuroscience research with respect to engagement objectives, communication and scope. This approach offers a foundation for sustaining relationships between scientists and communities rooted in trust and transparency, to ensure that impacts of neurotechnology on human health and cognition are aligned with patient needs as well as desired public values.

BackgroundEvidence on the long-term impact of urban green and blue spaces (UGBS) interventions remains limited. This study is a 15-year evaluation of an urban greenway development in Belfast (United Kingdom), assessing the potential effects of this UGBS intervention on physical activity (PA), mental wellbeing and co-benefits. MethodsUsing quasi-experimental design, a repeated cross-sectional survey was conducted in 2010 (baseline), 2017 (post-opening) and 2023 (long-term follow-up) with about 1,200 adults participated each wave. Outcomes included PA, mental wellbeing, general health, quality of life, social capital and environmental perception. Multilevel mixed-effect regressions were performed to examine within-group changes at long-term follow-up. Difference-in-differences analysis investigated the between-group changes that might be attributed to the greenway. Additional comparative analyses included distance-decay analysis and comparison with population trends in Northern Ireland. ResultsAt six years after completion, the greenway intervention appears to buffer a decline in duration of PA - mainly from moderate-intensity activity (decline lower by 118.6 min/week, 95%CI: 3.9-232.2) but with no significant impact on the proportion of the population meeting the recommended PA level. The intervention is associated with a smaller decline in self-rated health (4.98 units; 95%CI: 0.62-9.34) relative to control group. Intervention association with mental wellbeing was positive but not significant (p=0.30). The greenway also showed positive effects on social capital and environmental perceptions, with impacts most evident in improving safety and trust in the local area. ConclusionThis study provides evidence to support the public health impact of UGBS and its long-term health and social benefits.

Background Comprehensive sexuality education (CSE) is essential to the health and well-being of young people. In the Democratic Republic of Congo (DRC), where more than 65% of the population is under the age of 25, access to interpersonal CSE remains limited owing to sociocultural and structural barriers. This exposes young people to persistent socio-sanitary vulnerabilities. In this context, mobile health apps (MHAs) constitute a promising solution, supported by the growing use of smartphones among young Congolese. However, this group's intention to use MHAs for CSE has been the subject of little research to date. Objective The aim of this study was to identify predictors of intention to use MHAs among young Congolese, based on the extended Unified Theory of Acceptance and Use of Technology (UTAUT2). Methods A predictive correlational study was conducted in eight public secondary schools in Bukavu (DRC) with a stratified random sample of 859 students. Predictors of intention to use--performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FC), and perceived risk (PR)--and moderators--age, gender, and past MHA experience--were measured from data collected through a self-administered UTAUT questionnaire. Descriptive and multivariate analyses were run on SPSS version 28. Results Mean age of participants was 16.3 years (SD = 1.5). Boys made up 55.1% of the sample. Overall, 51.0% of the sample owned a smartphone, of which 62.3% reported having easy access to mobile data and 16.2% were already using MHAs to learn about sexual health. Intention to use MHAs was positively influenced by PE ({beta} = 0.523, p < 0.001), EE ({beta} = 0.115, p < 0.001), and SI ({beta} = 0.113, p < 0.001). FC (p = 0.260) and PR (p = 0.631), however, had no significant influence. Age moderated all of the relationships tested (F (1, 849-854) = 9.97-20.82; p [&le;] 0.002), with more marked effects observed among younger participants 14-15 years old. The final model explained 44% of the variance, indicating good predictive power. Conclusion Intention to use digital CSE was explained primarily by PE, EE, and SI and moderated by age. To strengthen this intention, stakeholders will need to promote e-interventions that are pertinent, easy to use, socially valorized, and tailored to young people's needs and to the local context.

Genetic counselling outcome measures are increasingly adapted for diverse clinical contexts. While the Genetic Counselling Outcome Scale (GCOS-24) is available in Swedish, no autism-specific version has been developed. Therefore, we adapted the Swedish GCOS-24 using the English version of the modified GCOS-24 (mGCSOS-24) to create a Swedish autism-specific mGCOS-24. Thereafter, we evaluated both the Swedish autism mGCOS-24 and the Swedish general GCOS-24 using Rasch analysis to assess their psychometric properties. Both instruments exhibited structural challenges, including multidimensionality, disordered thresholds, local item dependence, and invariance issues. For the Swedish autism mGCOS-24, we were able to identify subscales with acceptable measurement properties. However, applying the same structure to the Swedish general GCOS-24 did not resolve its broader limitations. This study introduces the first Swedish autism-specific mGCOS-24 and represents the first Rasch-based evaluation of any GCOS-24 or mGCOS-24 in Swedish. Our findings highlight important opportunities for measure refinement but also indicate that new or more substantially adapted tools may be needed to capture outcomes of genetic counselling in autistic populations.

Female genital cutting (FGC) is identified within global health and human rights discourse as aligned with gender inequality and female disempowerment. The persistence of FGC in high-prevalence societies is assumed to reflect womens limited influence over decisions concerning their daughters. Yet anthropological research has questioned whether this interpretation adequately reflects how FGC is organized within practicing communities. Across two studies with 176,728 participants from 15 African and Asian countries, we examine whether mothers attitudes toward FGC predict daughters circumcision status and whether this relationship varies with regional FGC prevalence. Multilevel logistic regression models show that maternal attitudes strongly predict daughter circumcision status across both datasets. Contrary to expectations derived from disempowerment frameworks, the association between maternal attitudes and daughter outcomes is not weaker in high-prevalence contexts, it is stronger. These findings suggest that interpretations of FGC as reflecting female disempowerment may mischaracterize the social dynamics of societies in which FGC is common. Policy implications of the findings are discussed.

Background: The enterotype concept proposed that gut microbiomes cluster into discrete types, but subsequent critiques demonstrated that such clustering depends on methodological choices, that the number of clusters is not fixed, and that faecal samples cannot capture spatial heterogeneity along the gastrointestinal tract. The stomach remains particularly understudied, and no systematic classification exists for gastric microbial community types. Methods: We assembled a multi-cohort dataset of 566 gastric mucosal samples spanning healthy controls to gastric cancer, with both Helicobacter pylori (HP)-negative and HP-positive individuals. Critically, we applied the key methodological lessons of the enterotype debate: we used a variational autoencoder (VAE) for dimensionality reduction to learn a continuous latent representation without forcing discrete structure, determined the optimal number of clusters using the Silhouette index (an absolute validation measure) across K=2 to K=10 rather than arbitrarily selecting a cluster number, and performed transparent evaluation of multiple clustering solutions. This VAE-plus-silhouette workflow directly addresses the critiques leveled against the original enterotype analysis. Results: Four gastotypes were identified, with K=4 achieving the highest mean silhouette score, indicating good cluster cohesion and separation. Two gastotypes (Variovorax-type and Trabulsiella-type) were significantly enriched in HP-positive samples, while two gastotypes (Bacteroides-type and Streptococcus-type) were significantly enriched in HP-negative samples. Random Forest and Gradient Boosting achieved excellent baseline performance for predicting HP infection (AUC = 0.990 and 0.993). Conclusions: The VAE-plus-silhouette workflow provides a robust, data-driven approach for identifying gastotypes without forcing discrete structure or arbitrarily fixing cluster numbers. Using this framework, we identified four gastotypes with significantly different HP infection rates. Variovorax-type and Trabulsiella-type showed strong HP-positive enrichment, while Bacteroides-type and Streptococcus-type showed strong HP-negative enrichment. These findings demonstrate that methodological advances from the enterotype controversy can be successfully transferred to the stomach, offering a reproducible taxonomy for stratifying HP infection status with potential clinical utility.

Severe mental disorders (SMDs), including bipolar disorder, schizophrenia, and major depressive disorder, are highly complex conditions associated with a substantial clinical burden and an increased suicide risk. Here, we present the Madrid Manic Cohort (MadManic), a large-scale initiative from Spain designed to integrate genomic, multi-omics, clinical, and digital phenotyping data to investigate the biological basis and clinical heterogeneity of SMDs. The cohort is still expanding and currently includes over 4,400 participants (~2,300 psychiatric patients and ~2,100 controls) and >11,000 biospecimens. Genotyping, transcriptomic and epigenetic data are available for different subsets of the cohort. By establishing the MadManic cohort we aim to integrate molecular data with detailed clinical and longitudinal digital information, allowing a more precise characterization of patient subgroups based on biological and phenotypic profiles. The MadManic cohort is well positioned to contribute to major international efforts in psychiatric genetics by enhancing the representation of Southern European populations, and advancing the identification of genetic risk, clinical predictors, and pharmacogenomic markers of treatment response. This cohort represents a valuable resource for advancing precision psychiatry, with the potential to improve risk prediction and guide personalized interventions in SMDs.

Benzalkonium chloride (BAC) is widely used as a disinfectant in cleaning products and is frequently detected in indoor dust. In this study, we assessed dust samples, along with information on cleaning product use, from 24 pregnant participants. Dust samples were analyzed for BAC concentration and microbial tolerance. Different chain lengths of BAC (C12, C14, and C16) were quantified using LC-MS/MS, and bacterial isolates were tested for BAC tolerance using minimum inhibitory concentration (MIC) assays. BAC was ubiquitously detected, with C12 and C14 being dominant. Higher BAC concentrations were associated with reported disinfectant use and increased microbial tolerance. These findings suggest that indoor antimicrobial use may promote microbial resistance, highlighting potential exposure risks in indoor environments and the need for further investigation into health and ecological impacts.

BackgroundThe COVID-19 pandemic exposed critical shortages of mechanical ventilators, particularly in low-resource settings. Disruptions in global supply chains and dependence on specialized components highlighted the need for scalable, locally manufacturing alternatives for emergency respiratory support. AimTo describe and evaluate a simplified, supply-chain-independent mechanical ventilator assembled from widely available automotive and simple hardware components, and intended as a last-resort solution. MethodsThe ventilator is based on a reciprocating air pump driven by an automotive windshield wiper motor coupled to parallel shaft bellows and readily assembled passive membrane valves, only requiring materials available from standard hardware retailers, minimal tools, and basic manual skills. Ventilator performance was assessed through bench testing using a patient model simulating severe lung disease in an adult (R=20 cmH2O{middle dot}s/L, C=15 mL/cmH2O) and pediatric (R=50 cmH2O{middle dot}s/L, C=10 mL/cmH2O) patients. Realistic proof of concept was performed in four mechanically ventilated 50-kg pigs. ResultsThe device delivered tidal volumes up to 600 mL and respiratory rates up to 45 breaths/min with PEEP up to 10 cmH2O, covering pediatric and adult ventilation ranges. In vivo testing showed that the ventilator maintained arterial blood gases within the targeted range. Technical details for ventilator construction are provided in an open-source video tutorial. DiscussionThis low-cost ventilator demonstrated adequate performance under demanding conditions. Although not a substitute for commercial intensive care ventilators, its simplicity, autonomy, and independence from fragile supply chains provide a potentially life-saving option in resource-constrained emergency scenarios.

BackgroundDental caries and periodontal disease represent the most prevalent global oral health conditions, collectively affecting several billion people. The diagnostic interpretation of dental radiographs, a cornerstone of modern dentistry, is associated with considerable inter-observer variability. In routine clinical practice, clinicians are required to evaluate a high volume of radiographic images daily, a cognitively demanding task in which diagnostic fatigue, time constraints, and the inherent complexity of overlapping anatomical structures can lead to the inadvertent oversight of early-stage pathologies. Artificial intelligence (AI) offers a transformative opportunity to augment clinical decision-making by providing rapid, objective, and consistent radiographic analysis, thereby serving as a tireless adjunct capable of flagging findings that may be missed during routine human inspection. MethodsThis study developed and validated a deep learning system for the automated detection of dental caries and alveolar bone loss using a dataset of 1,063 periapical and bitewing radiographs. Two separate YOLOv8s object detection models were trained and evaluated using a rigorous 5-fold cross-validation methodology. To align with the clinical use-case of a screening tool where high sensitivity is paramount, a custom image-level evaluation criterion was employed: a true positive was recorded if any predicted bounding box had a Jaccard Index (IoU) > 0 with any ground truth annotation. Model performance was systematically evaluated at confidence thresholds of 0.10 and 0.05. ResultsAt a confidence threshold of 0.05, the caries detection model achieved a mean precision of 84.41% ({+/-}0.72%), recall of 85.97% ({+/-}4.72%), and an F1-score of 85.13% ({+/-}2.61%). The alveolar bone loss model demonstrated exceptionally high performance, with a mean precision of 95.47% ({+/-}0.94%), recall of 98.60% ({+/-}0.49%), and an F1-score of 97.00% ({+/-}0.46%). ConclusionThe YOLOv8-based models demonstrated high accuracy and high sensitivity for detecting dental caries and alveolar bone loss on periapical radiographs. The system shows significant potential as a reliable automated assistant for dental practitioners, helping to improve diagnostic consistency, reduce the risk of missed pathology, and ultimately enhance the standard of patient care.

Aims Indoor air pollution resulting from combustion of unclean cooking fuels has been linked to adverse health outcomes, but evidence regarding its association with mental health in low- and middle-income countries remains limited. We investigated the association between household use of unclean cooking fuels, as a proxy for indoor air pollution, and depression symptoms among adults aged 45 years and older in India, and assessed effect modification by age, sex, caste, and rural/urban residence. Methods We conducted a cross-sectional analysis of the first wave (2017-2018) of data from the Longitudinal Aging Study in India (LASI), a nationally representative survey of adults aged [&ge;]45 years. Cooking fuel type was classified as clean or unclean, and depression symptoms were assessed using the 10-item Centre for Epidemiologic Studies Depression (CES-D-10) scale. We used logistic regression to estimate odds ratios for depression symptoms, and linear regression to compare mean CES-D-10 scores by cooking fuel type, adjusting for sociodemographic and housing characteristics. Results We included 62,650 respondents. Median age was 57 years (IQR: 50-65), 46.7% were women, 47.6% reported using unclean cooking fuels, and 27.6% screened positive on the CES-D-10. After adjusting for sociodemographic and housing characteristics, use of unclean cooking fuels was associated with higher odds of screening positive on the CES-D-10 (aOR: 1.08; 95% CI: 1.02, 1.15), and higher mean CES-D-10 scores (adjusted mean difference: 0.34; 95% CI: 0.24, 0.44). The association was more pronounced among individuals living in urban areas (aOR: 1.36; 95% CI: 1.21, 1.53). Conclusion Use of unclean cooking fuels was associated with depression symptoms among older adults in India, and especially among those living in urban areas.

Aims: Youth loneliness is a prevalent global health concern with lifelong health ramifications. Schools, as children's primary peer environments, are promising settings for loneliness interventions. However, school-based interventions are highly heterogeneous and no review to date has evaluated their effect on loneliness specifically. Methods: A systematic review was conducted to identify studies of school-based interventions measuring loneliness as an outcome in children and young people aged up to 18. Meta-analyses were conducted using a random-effects model to pool effect sizes and examine the significance of intervention characteristics and study design. Reported implementation factors were extracted and narratively synthesised. Results: Thirty-eight studies were included in meta-analysis, of which 19 were randomized controlled trials, ten were non-randomized controlled, and nine were single group studies. A small-to-moderate effect estimate was found, Hedges' g = -0.42 [95% CI: -0.71, -0.13], p = .006, and sub-group analyses indicated that differences in study design and quality did not result in significantly different effect estimates. Psychological interventions, followed by social and emotional skills training, produced significantly higher effects estimates compared with other intervention types. Conclusions: Findings indicate that school-based interventions are effective in reducing youth loneliness. However, study heterogeneity, reporting inconsistencies, and a wide prediction interval indicates this finding should be interpreted with caution. Future research may benefit from improved measurement and reporting of implementation factors, particularly dosage and fidelity.

Menstrual cycles are major biological events with extensive effects on the brain and cognition, experienced by half of the human population. To develop a comprehensive account of human cognition, it is necessary to successfully integrate and characterize menstrual cycle effects in cognitive science research. However, current approaches to menstrual cycle analysis suffer from low data resolution and are not well-equipped to capture the highly variable, non-linear changes in outcomes of interest across the cycle. We present a validated standardized method remedying these issues, demonstrate its utility using hormonal, behavioral, and neuroimaging data, and provide an open-source toolkit to facilitate its use.

Background: Understanding the underlying mechanisms for differences in vaccine uptake between migrants and non-migrants is crucial in order to design targeted interventions encouraging vaccination and to ensure vaccine-related equity. Therefore, this study examined to what extent migration-related disparities in COVID-19 vaccination were associated with psychological factors, based on the established 5C model of vaccine behaviour (Confidence, Complacency, Constraints, Calculation, Collective Responsibility). Methods: Data were obtained from the German study "Corona Monitoring Nationwide - Wave 2" (RKI-SOEP-2 study), which was carried out between November 2021 and March 2022. The association between COVID-19 vaccination and migration status, while considering the psychological factors, was investigated using multivariable binary logistic regressions. A decomposition analysis (Karlson-Holm-Breen method) was conducted to examine the extent to which migration-related disparities in vaccine uptake were associated with the psychological factors of the 5C framework. Results: Migrants were less likely to be vaccinated against COVID-19 compared to non-migrants, especially participants from the Middle East and North Africa (MENA) region. Our decomposition showed that almost two-thirds of the disparities in COVID-19 vaccine uptake between migrants and non-migrants were associated with the psychological factors (first-generation: 61.2%, second-generation: 64.2%). Confidence in safety of the vaccine was the most relevant factor in the 5C framework. Furthermore, the results highlighted the importance of a differentiated analysis regarding country of origin: While the 5C model accounted for only 19.4% of the difference between participants from the MENA region and non-migrants, the proportion for participants from Eastern Europe was 73.5%, suggesting that the underlying mechanisms for the lower uptake in the MENA group need further investigation. Conclusions: Overall, migration-related disparities in COVID-19 vaccination were significantly associated with differences in psychological factors of vaccine behaviour. To increase vaccine acceptance within the heterogeneous group of migrants in general, tailored and proactive health communication interventions are needed.

Background Higher levels of sedentary behaviour, such as leisure screen time (LST), and lower levels of physical activity are associated with diseases across multiple body systems which contribute to a large global health burden. Whether these associations are causal is unclear. The primary aim of this study is to investigate the causal effects of higher LST (given greater power) and, secondarily, lower moderate-to-vigorous intensity physical activity (MVPA), on a wide range of diseases in a hypothesis-free approach. Methods A two-sample Mendelian randomisation phenome-wide association study was conducted for the main analyses. Genetic single nucleotide polymorphisms (SNPs) were first selected as exposure genetic instruments for LST (hours of television watched per day; 117 SNPs) and MVPA (higher vs. lower; 18 SNPs) based on the genome-wide significant threshold (p < 5*10-8) from the largest relevant genome-wide association study (GWAS). For disease outcomes, we used summary results from FinnGen GWAS, including 1,719 diseases defined by hospital discharge International Classification of Diseases (ICD) codes in 453,733 European participants. For the main analyses, we used the inverse-variance weighting method with a Bonferroni corrected p-value of p [&le;] 3.47*10-4. Sensitivity analyses included Steiger filtering, MR-Egger and weighted median analyses, and data from UK Biobank were used to explore replication. Findings Genetically predicted higher LST was associated with increased risk of 87 (5.1% of the 1,719) diseases. Most of these diseases were in musculoskeletal and connective tissue (n=37), genitourinary (n=12) and respiratory (n=8) systems. Genetic liability to lower MVPA was associated with six diseases: three in musculoskeletal and connective tissue and genitourinary systems (with greater risk of these diseases also identified with higher LST), and three in respiratory and genitourinary systems. Sensitivity analyses largely supported the main analyses. Results replicated in UK Biobank, where data available. Conclusions Higher levels of sedentary behaviour, and lower levels of physical activity, causally increase the risk of diseases across multiple body systems, making them promising targets for reducing multimorbidity.

This study investigated retirement adjustment in retired police officers in the UK (N = 289), examining how time since leaving the service moderates the relationship between perceived organisational support and retirement adjustment while accounting for resilience. Results indicated a developmental trend: organisational support remains stable initially but becomes increasingly influential in later life. Using Johnson-Neyman analysis, a threshold of 32.07 years was identified, after which the association reaches statistical significance. These findings suggest an organisational legacy effect; for the older generation, the retrospective perception of being valued by the service acts as a durable psychological resource. This study offers a novel conceptualisation of long-term organisational influence by identifying a temporally delayed legacy effect that extends beyond existing models of retirement adjustment. The study advocate for lifelong wellbeing strategies that extend, recognising that the organisational relationship continues to shape adjustment outcomes decades after the conclusion of active duty.