Biology subjects

Hillsdon, M.

Publications and source records attributed to Hillsdon, M..

3 recordsLinked to original sources

bioRxiv2026

Validation and optimisation of wearable accelerometer data pre-processing for digital measure implementation and development

The increasing use of accelerometers as digital health technologies in clinical trials and clinical care is driving the need for data processing to meet medical standards. The aim of this study was to create and test a modular pipeline for the pre-processing of high-resolution accelerometry that assures the quality, transparency and traceability of digital measures from sensor-level data. The objective is for the pipeline to be a foundational layer in the development, implementation and comparison of measures. The study developed the open GENEAcore package to meet the requirements of regulators, verifying the engineering implementation and analytically validating outputs against reference datasets. Early stages included the optimisation of calibration and non-wear detection. Data-driven detection of behavioural transitions was then validated to give direct bout outputs without the need to identify rules for epoch aggregation and interruptions. The utility for measure development was shown by comparing two algorithms for the characterisation of activity intensity in both the epoch and bout paradigms. Non-wear was detected with a balanced accuracy of 92.3% and the commonly used 13mg acceleration standard deviation threshold was empirically validated for the first time. The detection of transitions proved reliable with 99% detected, on average, within 2 seconds of their occurrence to give a mean expected event duration of 68.6s from a log-normal distribution. The different activity intensity algorithms were more than 99% concordant during movement but their outputs diverged in low movement conditions. Importantly, variable duration bouts created 31% higher daily activity durations compared to 1-second epochs. This evaluation of pre-processing steps has confirmed the attention to detail required to create robust and reproducible results for later clinical validation where small changes in an algorithm or its implementation may have clinically meaningful consequences.

animal behavior and cognition↗

bioRxiv2018

Genetic studies of accelerometer-based sleep measures in 85,670 individuals yield new insights into human sleep behaviour

Sleep is an essential human function but its regulation is poorly understood. Identifying genetic variants associated with quality, quantity and timing of sleep will provide biological insights into the regulation of sleep and potential links with disease. Using accelerometer data from 85,670 individuals in the UK Biobank, we performed a genome-wide association study of 8 accelerometer-derived sleep traits, 5 of which are not accessible through self-report alone. We identified 47 genetic associations across the sleep traits (P<5x10-8) and replicated our findings in 5,819 individuals from 3 independent studies. These included 26 novel associations for sleep quality and 10 for nocturnal sleep duration. The majority of newly identified variants were associated with a single sleep trait, except for variants previously associated with restless legs syndrome that were associated with multiple sleep traits. Of the new associated and replicated sleep duration loci, we were able to fine-map a missense variant (p.Tyr727Cys) in PDE11A, a dual-specificity 3,5-cyclic nucleotide phosphodiesterase expressed in the hippocampus, as the likely causal variant. As a group, sleep quality loci were enriched for serotonin processing genes and all sleep traits were enriched for cerebellar-expressed genes. These findings provide new biological insights into sleep characteristics.

genetics↗

bioRxiv2018

Genome-wide association analyses of chronotype in 697,828 individuals provides new insights into circadian rhythms in humans and links to disease

Using genome-wide data from 697,828 research participants from 23andMe and UK Biobank, we increase the number of identified loci associated with being a morning person, a behavioural indicator of a persons underlying circadian rhythm, from 24 to 351. Using data from 85,760 individuals with activity-monitor derived measures of sleep timing we show that the chronotype loci influence sleep timing: the mean sleep timing of the 5% of individuals carrying the most "morningness" alleles was 25 minutes earlier than the 5% carrying the fewest. The loci were enriched for genes involved in circadian regulation, cAMP, glutamate and insulin signalling pathways, and those expressed in the retina, hindbrain, hypothalamus, and pituitary. We provide evidence that being a morning person is causally associated with better mental health but does not appear to affect BMI or Type 2 diabetes. This study offers new insights into the biology of circadian rhythms and links to disease in humans.

genetics↗