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Introduction:New-onset atrial fibrillation (NOAF) is a common arrhythmia in patients on an intensive care unit (ICU). NOAF in this setting is associated with adverse short and long-term outcomes. Little is known about modifiable risk factors for NOAF. Developing a model to identify patients at high risk of developing NOAF is vital for future studies investigating prevention strategies to allow stratification, sample enrichment and efficient study design. Methods:We will use data from 7 general ICUs in the UK and USA to develop a predictive model for NOAF. We will assess whether including longitudinal, dynamic predictors, along with the use of machine learning approaches improves model predictive ability. Generalisability and Implications:This work will be the largest assessment of predictors of NOAF in patients treated on a general ICU. It will be the first to analyse international data to ensure worldwide applicability. A detailed understanding of modifiable risk factors underpins future work to prevent NOAF in these patients.

Non-invasive foetal electrocardiography (NI-FECG) has become an important prenatal monitoring method in the hospital. However, due to its susceptibility to non-stationary noise sources and lack of robust extraction methods, the capture of high-quality NI-FECG remains a challenge. Recording waveforms of sufficient quality for clinical use typically requires human visual inspection of each recording. A Signal Quality Index (SQI) can help to automate this task but, contrary to adult ECG, work on SQIs for NI-FECG is sparse. In this paper, a multi-channel signal quality classifier for NI-FECG waveforms is presented. The model can be used during the capture of NI-FECG to assist technicians to record high-quality waveforms, which is currently a labour-intensive task. A Convolutional Neural Network (CNN) is trained to distinguish between NI-FECG segments of high and low quality. NI-FECG recordings with one maternal channel and three abdominal channels were collected from 100 subjects during a routine hospital screening (102.6amp;nbsp;min of data). The model achieves an average 10-fold cross-validated AUC of 0.95amp;nbsp;±amp;nbsp;0.02. The results show that the model can reliably assess the FECG signal quality on our dataset. The proposed model can improve the automated capture and analysis of NI-FECG as well as reduce technician labour time.

Food intake monitoring can play an important role in the prevention of malnutrition among older adults. Traditional monitoring methods typically involve the use of pen-and-paper food diaries or questionnaires. While digital alternatives exist, these tools rely on manual data entry, often multiple times a day. Furthermore, the recorded data may be incomplete and contain mistakes due to human error or a deliberate misreporting of the food intake. While these methods are considered to be the gold standard for food intake, they are rarely used in the ageing population due to their time consuming nature. Nevertheless, nutrition plays an important role in the health status of older adults. Malnutrition has been linked with decreased muscle strength, poorly healing wounds, increased hospital admission time and increased hospital mortality rate. Preventing malnutrition by means of a targeted nutritional intervention can prevent these problems and increase general quality of life. A routine monitoring for early recognition and treatment of malnutrition should therefore be included in the routine care of older adults. Technology can play an important role in the food monitoring process. Sensors may be employed to automatically measure eating activity or the amount of consumed food, which may supplement traditional methods, lowering the recording burden on the user and caregiver. Several sensor systems have been proposed in the literature to accomplish this task, from wearable devices to table embedded scales and camera based methods running on a smartphone. Research specifically investigating methodologies for use in the older population, however, remains sparse. The wearable systems proposed in the literature may not be comfortable for use over longer periods of time or be stigmatising to the older adult, table embedded scales require an extensive adaptation of the existing eating surface, while smartphone usage has proven difficult in older adults. There is an acute need for tools that are comfortable to use by older adults to aid in the daily care and prevention or treatment of malnutrition or related disease. Two sensor systems and corresponding algorithms are proposed in this thesis. The first is an accelerometer based wearable system, with the accelerometer mounted on the eyeglasses of the user. The eyeglasses are used in this context as a platform to mount the accelerometer in close proximity to the head. The ears of the glasses are able to transmit the vibrations and movements of mastication muscles in the skull during chewing to the accelerometer, where it can be converted into an acceleration signal and used for the detection of chewing activity. A chewing detection algorithm is proposed based on supervised machine learning techniques. Data was recorded from older adults in a nursing home to train and validate the model. The results show that an accelerometer worn this way can be used to detect chewing activity. Furthermore, a smart plate system is presented. For the purpose of the research, a prototype was designed and developed consisting of a custom embedded system and sensors. The system consists of a base station and an off-the-shelf polymer plate that is mounted on top of the base. Weight sensors in the base accurately measure the weight of consumed food from the plate. The novelty of this system is the ability to measure the location of individual bites on the plate. In combination with a compartmentalised plate, the system can estimate from which compartment a bite was taken, without any sensors or electronics embedded into the plate itself or physical changes to the plate. All hardware is located into the base station and is separate from the plate. For the bite localisation to work, an accurate detection of the individual bites is required. For this, a novel bite detection algorithm is presented based on a supervised learner. Data was recorded from older adults eating a meal with the plate in a nursing home and hospital, and is used to train and validate the model. Results show that the system works as expected, with a bite detection algorithm that improves on the state of the art and the ability to measure the consumed food per compartment. With prior knowledge of which food type was served in which compartment, this can allow for an accurate estimation of the total amount of ingested calories. Finally, an exploratory study into behavioural analysis using the smart plate is presented. We show that parameters extracted from individual bites detected with the smart plate may be used as a descriptor of behavioural traits during eating. The systems and algorithms presented in the thesis have the potential to lower the threshold for the adaptation of sensor based food intake monitoring in older adults. Furthermore, the tools can be employed for research in other target groups, from prevention and treatment of obesity to quantified self applications in young to middle aged adults. status: published

Christoph Rosak,1 Gabriele Mertes21Specialist for Internal Medicine, Endocrinology and Diabetes, Sachsenhausen Hospital, Frankfurt/Main, Germany; 2Retired, Mülheim, GermanyAbstract: The alpha-glucosidase inhibitor acarbose has been used for more than 20 years in the management of hyperglycemia. Owing to its unique mode of action in the gastrointestinal tract, its properties are very different from other antidiabetic medications. Patients on long-term treatment to control a chronic disease are not only interested in good treatment efficacy, but are also even more interested in the safety and side effects of their medications. Significant aspects of acarbose predominantly regarding safety and tolerability in the management of type 2 diabetes and prediabetes are reviewed. It is concluded that acarbose is a convenient long-term treatment option, with benefits for both type 2 diabetics and patients in a prediabetic state.Keywords: acarbose, prediabetes, type 2 diabetes, patient considerations, side effects