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University students are at risk of experiencing mental health and diet quality problems during their transition to university. This study aimed to examine the bidirectional associations between the diet quality and mental health of students during their transition to universities in the UK, and the impact of the transition on diet quality and mental health. The study adopted a cross-sectional design and took place during the first semester of year 2021-2022. Participants were first year undergraduate students at four UK universities, living at student halls. They were invited to participate by completing online surveys, including a diet quality instrument (Short Form Food Frequency Questionnaire), a mental health instrument (Depression Anxiety and Stress Scale) and a quality of transition instrument (College Adjustment Questionnaire). Results showed that a healthy diet was associated with good mental health (b = -3.46, 95% CI [-6.14, -.78]). Unfavourable mental health was associated with having an unhealthy diet (b = -.01, 95% CI [-.02, -.00]). Transition did not moderate the relationship between diet quality and mental health of students, or vice versa (ps> .05). Preliminary analyses showed a significant direct effect of good quality of transition to university on good mental health (b = -1.51, 95% CI [-1.88, -1.13]), but did not have an indirect effect via diet quality; there were no significant direct or indirect effects on diet quality, however larger studies are needed to replicate these preliminary analyses. Our current findings can inform university policies and health education research, and further research is needed to determine whether interventions to improve diet quality at the university level could reduce mental health issues, and whether interventions to support students under stress may lead to healthier dietary habits. Cross-sectional studies cannot determine the directionality of effects, hence longitudinal studies are required to enhance our understanding of the relationships between diet quality and mental health over time., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Solomou et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Accelerometry data has been widely used to measure activity and the circadian rhythm of individuals across the health sciences, in particular with people with advanced dementia. Modern accelerometers can record continuous observations on a single individual for several days at a sampling frequency of the order of one hertz. Such rich and lengthy data sets provide new opportunities for statistical insight, but also pose challenges in selecting from a wide range of possible summary statistics, and how the calculation of such statistics should be optimally tuned and implemented. In this paper, we build on existing approaches, as well as propose new summary statistics, and detail how these should be implemented with high frequency accelerometry data. We test and validate our methods on an observed data set from 26 recordings from individuals with advanced dementia and 14 recordings from individuals without dementia. We study four metrics: Interdaily stability (IS), intradaily variability (IV), the scaling exponent from detrended fluctuation analysis (DFA), and a novel nonparametric estimator which we call the proportion of variance (PoV), which calculates the strength of the circadian rhythm using spectral density estimation. We perform a detailed analysis indicating how the time series should be optimally subsampled to calculate IV, and recommend a subsampling rate of approximately 5 minutes for the dataset that has been studied. In addition, we propose the use of the DFA scaling exponent separately for daytime and nighttime, to further separate effects between individuals. We compare the relationships between all these methods and show that they effectively capture different features of the time series., Competing Interests: The authors have declared that no competing interests exist.