Digital remote monitoring for screening and early detection of urinary tract infections.

Urinary Tract Infections (UTIs) are one of the most prevalent bacterial infections in older adults and a significant contributor to unplanned hospital admissions in People Living with Dementia (PLWD), with early detection being crucial due to the predicament of reporting symptoms and limited help-seeking behaviour. The most common diagnostic tool is urine sample analysis, which can be time-consuming and is only employed where UTI clinical suspicion exists. In this method development and proof-of-concept study, participants living with dementia were monitored via low-cost devices in the home that passively measure activity, sleep, and nocturnal physiology. Using 27828 person-days of remote monitoring data (from 117 participants), we engineered features representing symptoms used for diagnosing a UTI. We then evaluate explainable machine learning techniques in passively calculating UTI risk and perform stratification on scores to support clinical translation and allow control over the balance between alert rate and sensitivity and specificity. The proposed UTI algorithm achieves a sensitivity of 65.3% (95% Confidence Interval (CI) = 64.3-66.2) and specificity of 70.9% (68.6-73.1) when predicting UTIs on unseen participants and after risk stratification, a sensitivity of 74.7% (67.9-81.5) and specificity of 87.9% (85.0-90.9). In addition, feature importance methods reveal that the largest contributions to the predictions were bathroom visit statistics, night-time respiratory rate, and the number of previous UTI events, aligning with the literature. Our machine learning method alerts clinicians of UTI risk in subjects, enabling earlier detection and enhanced screening when considering treatment.

The role of circadian phase in sleep and performance during Antarctic winter expeditions.

The Antarctic environment presents an extreme variation in the natural light-dark cycle which can cause variability in the alignment of the circadian pacemaker with the timing of sleep, causing sleep disruption, and impaired mood and performance. This study assessed the incidence of circadian misalignment and the consequences for sleep, cognition, and psychological health in 51 over-wintering Antarctic expeditioners (45.6 ± 11.9 years) who completed daily sleep diaries, and monthly performance tests and psychological health questionnaires for 6 months. Circadian phase was assessed via monthly 48-h urine collections to assess the 6-sulphatoxymelatonin (aMT6s) rhythm. Although the average individual sleep duration was 7.2 ± 0.8 h, there was substantial sleep deficiency with 41.4% of sleep episodes <7 h and 19.1% <6 h. Circadian phase was highly variable and 34/50 expeditioners had sleep episodes that occurred at an abnormal circadian phase (acrophase outside of the sleep episode), accounting for 18.8% (295/1565) of sleep episodes. Expeditioners slept significantly less when misaligned (6.1 ± 1.3 h), compared with when aligned (7.3 ± 1.0 h; p < .0001). Performance and mood were worse when awake closer to the aMT6s peak and with increased time awake (all p < .0005). This research highlights the high incidence of circadian misalignment in Antarctic over-wintering expeditioners. Similar incidence has been observed in long-duration space flight, reinforcing the fidelity of Antarctica as a space analog. Circadian misalignment has considerable safety implications, and potentially longer term health risks for other circadian-controlled physiological systems. This increased risk highlights the need for preventative interventions, such as proactively planned lighting solutions, to ensure circadian alignment during long-duration Antarctic and space missions.

Home monitoring of daily living activities and prediction of agitation risk in a cohort of people living with dementia.

BACKGROUND: People living with dementia (PLWD) have an increased susceptibility to developing adverse physical and psychological events. Internet of Things (IoT) technologies provides new ways to remotely monitor patients within the comfort of their homes, particularly important for the timely delivery of appropriate healthcare. Presented here is data collated as part of the on-going UK Dementia Research Institute's Care Research and Technology Centre cohort and Technology Integrated Health Management (TIHM) study. There are two main aims to this work: first, to investigate the effect of the COVID-19 quarantine on the performance of daily living activities of PLWD, on which there is currently little research; and second, to create a simple classification model capable of effectively predicting agitation risk in PLWD, allowing for the generation of alerts with actionable information by which to prevent such outcomes. METHOD: A within-subject, date-matched study was conducted on daily living activity data using the first COVID-19 quarantine as a natural experiment. Supervised machine learning approaches were then applied to combined physiological and environmental data to create two simple classification models: a single marker model trained using ambient temperature as a feature, and a multi-marker model using ambient temperature, body temperature, movement, and entropy as features. RESULT: There are 102 PLWD total included in the dataset, with all patients having an established diagnosis of dementia, but with ranging types and severity. The COVID-19 study was carried out on a sub-group of 21 patient households. In 2020, PLWD had a significant increase in daily household activity (p = 1.40e-08), one-way repeated measures ANOVA). Moreover, there was a significant interaction between the pandemic quarantine and patient gender on night-time bed-occupancy duration (p = 3.00e-02, two-way mixed-effect ANOVA). On evaluating the models using 10-fold cross validation, both the single and multi-marker model were shown to balance precision and recall well, having F1-scores of 0.80 and 0.66, respectively. CONCLUSION: Remote monitoring technologies provide a continuous and reliable way of monitoring patient day-to-day wellbeing. The application of statistical analyses and machine learning algorithms to combined physiological and environmental data has huge potential to positively impact the delivery of healthcare for PLWD.

The influence of miniscrew insertion torque.

Objective: The aim of this in vitro study was to evaluate the progressive development of surface microdamage produced following the insertion of orthodontic miniscrews (OMs) into 1.5 mm thick porcine tibia bone using maximum insertion torque values of 12 Ncm, 18 Ncm, and 24 Ncm. Methods: Aarhus OMs (diameter 1.5 mm; length 6 mm) were inserted into 1.5 mm porcine bone using a torque limiting hand screwdriver set at 12 Ncm, 18 Ncm, and 24 Ncm. A custom rig equipped with a compression load cell was used to record the compression force exerted during manual insertion. A sequential staining technique was used to identify microdamage viewed under laser confocal microscopy. Virtual slices were created and stitched together to form a compressed two-dimensional composition of the microdamage. Histomorphometric parameters, including total damage area, diffuse damage area, maximum crack length, maximum damage radius, and maximum diffuse damage radius, were measured. Kruskal-Wallis Tests and Wilcoxon Rank-Sum Tests were used to analyse the generated data. Results: All OMs inserted using 12 Ncm failed to insert completely, while partial insertion was observed for two OMs inserted at 18 Ncm. Complete insertion was achieved for all OMs inserted at 24 Ncm. Histomorphometrically, OMs inserted using 24 Ncm produced a significantly larger diffuse damage area (P < 0.05; P < 0.05) and maximum diffuse damage radius (P < 0.05; P < 0.05), for both the entry and exit surfaces, respectively, compared with the 12 Ncm and 18 Ncm groups. Conclusions: Insertion torque can influence the degree of OM insertion and, subsequently, the amount of microdamage formed following insertion into 1.5 mm thick porcine tibia bone. An increase in insertion torque corresponds with greater insertion depth and larger amounts of microdamage.

Influence of cortical bone thickness on miniscrew microcrack formation.

INTRODUCTION: The aim of this in-vitro study was to investigate the influence of cortical bone thickness on the amount of surface microdamage produced after insertion of orthodontic miniscrews (OM) in porcine tibia bone. METHODS: Aarhus OMs (Medicon, Tuttlingen, Germany; diameter, 1.5 mm; length, 6 mm) were inserted into 1.0 mm (group A; n = 10), 1.5 mm (group B; n = 10), and 2.0 mm (group C; n = 10) of porcine cortical bone using a torque-limiting hand screwdriver set at 18 Ncm. A sequential staining technique was used to identify microdamage under laser confocal microscopy. Virtual slices were stitched together using ImageJ software (National Institutes of Health, Bethesda, Md) to form a compressed 2-dimensional composition of the microdamage. The ImageJ software was used to quantify the total damage area, diffuse damage area, maximum crack length, maximum damage radius, and maximum diffuse damage radius. Kruskal-Wallis tests and Wilcoxon rank sum tests were used to analyze the data. RESULTS: All OMs in group A (1.0 mm) were inserted completely; however, 2 OMs from group B (1.5 mm) and all OMs in group C (2.0 mm) failed to insert completely. The entry surface of group C (2.0 mm) exhibited significantly higher amounts of total damage, diffuse damage area, maximum crack length, and maximum crack damage radius compared with groups A (1.0 mm) and B (1.5 mm). The maximum crack length observed on the entry and exit surfaces ranged from 1.03 to 3.06 mm. CONCLUSIONS: In this study, we demonstrated a higher level of microdamage after the insertion of OMs into 2.0-mm thick cortical bone compared with 1.0-mm thick cortical bone. Therefore, clinicians need to consider the thickness of the cortical bone at the insertion site, because mechanisms to reduce cortical bone thickness would likely reduce the amount of microdamage formed. A safety zone of 3.5 mm from the OM is also recommended for OMs inserted into 1.0- and 1.5-mm cortical bone thicknesses to minimize any detrimental effects after targeted remodeling.

Effects of fatigue induced damage on the longitudinal fracture resistance of cortical bone.

As a composite material, cortical bone accumulates fatigue microdamage through the repetitive loading of everyday activity (e.g. walking). The accumulation of fatigue microdamage is thought to contribute to the occurrence of fragility fractures in older people. Therefore it is beneficial to understand the relationship between microcrack accumulation and the fracture resistance of cortical bone. Twenty longitudinally orientated compact tension fracture specimens were machined from a single bovine femur, ten specimens were assigned to both the control and fatigue damaged groups. The damaged group underwent a fatigue loading protocol to induce microdamage which was assessed via fluorescent microscopy. Following fatigue loading, non-linear fracture resistance tests were undertaken on both the control and damaged groups using the J-integral method. The interaction of the crack path with the fatigue induced damage and inherent toughening mechanisms were then observed using fluorescent microscopy. The results of this study show that fatigue induced damage reduces the initiation toughness of cortical bone and the growth toughness within the damage zone by three distinct mechanisms of fatigue-fracture interaction. Further analysis of the J-integral fracture resistance showed both the elastic and plastic component were reduced in the damaged group. For the elastic component this was attributed to a decreased number of ligament bridges in the crack wake while for the plastic component this was attributed to the presence of pre-existing fatigue microcracks preventing energy absorption by the formation of new microcracks.