Cardiorespiratory fitness estimation using wearable sensors: laboratory and free-living analysis of context-specific submaximal heart rates

In this work, we propose to use pattern recognition methods to determine submaximal heart rate (HR) during specific contexts, such as walking at a certain speed, using wearable sensors in free-living, and use context-specific HR to estimate cardiorespiratory fitness (CRF). CRF of 51 participants was assessed by a maximal exertion test (VO2max). Participants wore a combined accelerometer and HR monitor during a laboratory based simulation of activities of daily living and for two weeks in free-living. Anthropometrics, HR while lying down and walking at predefined speeds in laboratory settings were used to estimate CRF. Explained variance (R2) was 0.64 for anthropometrics, and increased up to 0.74 for context-specific HR (0.73 to 0.78 when including fat-free mass). Then, we developed activity recognition and walking speed estimation algorithms to determine the same contexts (i.e. lying down and walking) in free-living. Context-specific HR in free-living was highly correlated with laboratory measurements (Pearson's r = 0.71-0.75). R2 for CRF estimation was 0.65 when anthropometrics were used as predictors, and increased up to 0.77 when including free-living context-specific HR (i.e. HR while walking at 5.5 km/h). R2 varied between 0.73 and 0.80 when including fat-free mass among the predictors. RMSE was reduced from 354.7 ml/min to 281.0 ml/min by the inclusion of context-specific HR parameters (21% error reduction). We conclude that pattern recognition techniques can be used to contextualize HR in free-living and estimated CRF with accuracy comparable to what can be obtained with laboratory measurements of HR response to walking

Digital phenotyping through multimodal, unobtrusive sensing

The growing adoption of multimodal wearable and mobile devices, such as smartphones and wrist-worn watches has generated an increase in the collection of physiological and behavioural data at scale. This digital phenotyping data enables researchers to make inferences regarding users’ physical and mental health at scale, for the first time. However, translating this data into actionable insights requires computational approaches that turn unlabelled, multimodal time-series sensor data into validated measures that can be interpreted at scale. This thesis describes the derivation of novel computational methods that leverage digital phenotyping data from wearable devices in large-scale populations to infer physical behaviours. These methods combine insights from signal processing, data mining and machine learning alongside domain knowledge in physical activity and sleep epidemiology. First, the inference of sleeping windows in free-living conditions through a heart rate sensing approach is explored. This algorithm is particularly valuable in the absence of ground truth or sleep diaries given its simplicity, adaptability and capacity for personalization. I then explore multistage sleep classification through combined movement and cardiac wearable sensing and machine learning. Further, I demonstrate that postural changes detected through wrist accelerometers can inform habitual behaviours and are valuable complements to traditional, intensity-based physical activity metrics. I then leverage the concomitant responses of heart rate to physical activity that can be captured through multimodal wearable sensors through a self-supervised training task. The resulting embeddings from this task are shown to be useful for the downstream classification of demographic factors, BMI, energy expenditure and cardiorespiratory fitness. Finally, I describe a deep learning model for the adaptive inference of cardiorespiratory fitness (VO2max) using wearable data in free living conditions. I demonstrate the robustness of the model in a large UK population and show the models’ adaptability by evaluating its performance in a subset of the population with repeated measures ~6 years after the original recordings. Together, this work increases the potential of multimodal wearable and mobile sensors for physical activity and behavioural inferences in population studies. In particular, this thesis showcases the potential of using wearable devices to make valuable physical activity, sleep and fitness inferences in large cohort studies. Given the nature of the data collected and the fact that most of this data is currently generated by commercial providers and not research institutes, laying the foundations for responsible data governance and ethical use of these technologies will be critical to building trust and enabling the development of the field of digital phenotyping.I was funded by GlaxoSmithKline and the Engineering and Physical Sciences Research Council. I was also supported by the Alan Turing Institute through their Enrichment Scheme

Obesity and functional genomics-identified genes : a focus on the high-fat diet-induced gene trefoil factor 2 (Tff2) and the exercise-induced gene secreted protein acidic and rich in cysteine (Sparc) within the context of energy metabolism

L'obesité est un problème de santé en soi et c'est aussi un facteur de risque pour de nombreux autres problèmes de santé. Outre le contrôle de l'alimentation et l'activité physique, les options pharmacologiques contre l'obésité restent limitées et de nouvelles options thérapeutiques sont nécessaires. Dans ce contexte, la génomique fonctionnelle peut identifier de nouvelles options pour gérer et étudier l'obésité. Notre groupe de recherche a identifié deux gènes liés aux deux principaux facteurs ayant un impact sur le développement de l'obésité : La diète, principalement riche en gras (HFD) et l'exercice. Ces deux gènes clés sont le trefoil factor family member 2 (Tff2) et la secreted protein acidic and rich in cysteine (SPARC). Alors que Tff2 a été identifié comme un gène induit par la HFD, SPARC a été caractérisé comme un gène induit par l'exercice. Notre groupe de recherche a également constaté que les souris Tff2 knock-out (KO) sont protégées contre l'obésité induite par la HFD. Par conséquent, ma thèse explore Tff2 et Sparc dans le contexte de l'obésité et le métabolisme énergétique. Tff2 est principalement exprimé dans le système digestif où elle a une propriété de protection des muqueuses, tandis que Sparc est plus largement distribué et s'exprime principalement lors de remodelage tissulaire dans des situations telles que les blessures et la croissance. Les parties théoriques de cette thèse décrivent diverses propriétés de Tff2 et Sparc décrites dans la littérature telles que le métabolisme et les rôles cellulaires ainsi que les implications et les applications potentielles des données que j'ai générées. Pour mes données de recherche rapportées dans cette thèse, elles sont divisées en trois publications. La première, explore des souris Tff2 KO pour expliquer leur protection contre l'obésité induite par la HFD. Les souris Tff2 KO avaient des taux plus faibles de glucose, de triglycérides et de glycérol. Leurs niveaux d'expression génique et protéique indiquent moins de stockage de graisse et une dépense énergétique accrue en améliorant l'utilisation des lipides et du glucose via la phosphorylation oxydative. Nos données mettent en évidence les voies liées à Tff2 comme potentielles cibles pour les thérapies contre l'obésité. Via une expérimentation animale, la deuxième étude vise à identifier des implications de SPARC principalement dans le muscle dans les contextes de l'exercice. Les souris ont été divisées en huit groupes en fonction de trois variables (âge, génotype et exercice). Les effets du Sparc KO sur la composition corporelle, l'adiposité et le métabolisme sont vers une réduction du tissu adipeux blanc et du poids corporel, mais avec un phénotype métabolique et fonctionnel musculaire négatif. Alors que ces effets négatifs s'aggravent avec le vieillissement, ils sont relativement améliorés par l'exercice. Nos données suggèrent aussi que les changements induits par l'exercice dans le phénotype du muscle squelettique (métabolisme, force et développement), y compris les changements induits par le lactate, dépendent de SPARC. Le troisième article à deux parties. Tout d'abord, j'explore les conséquences du Sparc KO et les compare aux effets du vieillissement. J'observe également les effets de l'exercice. Dans la deuxième partie, j'étudie les effets de la surexpression de Sparc et les compare aux avantages de l'exercice. Les mesures étaient principalement liées au poids des tissus, à l'adiposité, au métabolisme et à la force musculaire. Collectivement, ces résultats, et les données de la deuxième étude, montrent que les souris Sparc KO développe un phénotype semblable au vieillissement, tandis que la surexpression de SPARC et l'exercice génèrent des avantages similaires. Ces avantages visent à contrer à la fois le phénotype du vieillissement induit par le déficit en SPARC et à améliorer les changements liés à l'âge. Les applications potentielles de ces résultats sont de construire/optimiser des modèles d'animaux basés sur Sparc KO et, d'autre part, de développer des thérapies contre l'obésité, les troubles métaboliques ou liés à l'âge basées sur l'introduction de SPARC ou le ciblage des voies liées à SPARC pour imiter l'exercice. L'exploration de telles voies moléculaires permettrait à la fois d'élucider certains mécanismes et de développer une nouvelle génération d'options thérapeutiques pour l'obésité et les troubles métaboliques, y compris les troubles liés à l'âge. De telles approches seraient basées sur le ciblage de TFF2, SPARC ou de leurs voies connexes.Obesity represents a challenge for health professionals. It is a health problem itself and it is also a risk factor for numerous health problems. Beside diet control and physical activity, the pharmacological options against obesity remain limited and novel therapeutic options are required. Within this context, functional genomics represents an emerging approach to identify novel options to manage and study obesity. Our research group has previously conducted functional genomics explorations to identify genes related to the two main factors impacting obesity development: Diet (mainly high fat) and exercise. Indeed, both diet, especially high-fat diet (HFD), and exercise are at the center of obesity management. Those functional genomics studies identified two key genes: Trefoil factor family member 2 (Tff2) and secreted protein acidic and rich in cysteine (SPARC). Whereas Tff2 was identified as a HFD-induced gene, SPARC was characterized as an exercise-induced gene. Following that, our research group has also found that Tff2 knock-out (KO) in mice protects them from the HFD-induced obesity. Therefore, my thesis explores Tff2 and Sparc within the context of obesity and energy metabolism. Tff2 is mainly expressed in the digestive system where it has mucus protection property, whereas Sparc is more widely distributed and is expressed mainly during tissues remodeling in situations such as injuries and growth. The theoretical parts of this thesis describe various properties of both Tff2 and Sparc reported in the literatures such as metabolism and cellular roles as well as implications and potential applications of the data I generated. For the research parts reported in this thesis, they are divided into three publications. The first one, explores Tff2 KO-related pathways of mice at the genomic, proteinic and biochemical levels to elucidate the processes behind their protection from the HFD-induced obesity. Tff2 KO mice had lower levels of serum glucose, triglycerides and glycerol. Western blotting and Q_RT-PCR revealed that the expression levels of selected genes and proteins are toward less fat storage and increased energy expenditure by enhancing lipid and glucose utilization via oxidative phosphorylation. The data highlight Tff2-related pathways as potential targets for obesity therapies. Via an animal experiment, the second study aims to identify selected implications of SPARC mainly within the muscle in the contexts of exercise. Mice were divided into eight groups based on three variables (age, genotype and exercise): Old or young × Sparc KO or wild type × sedentary or exercise. The exercised groups were trained before all mice were sacrificed. Sparc KO effects on body composition, adiposity and metabolic patterns are toward a reduced white adipose tissue and body weight, but with a negative metabolic and functional phenotype of the skeletal muscle. Whereas such negative effects on skeletal muscle are worsened with ageing, they are relatively improved by exercise. Importantly, our data suggest that the exercise-induced changes in the skeletal muscle phenotype, in terms of increased performance (metabolic, strength and development) including lactate-induced changes, are SPARC-dependent. The third paper studies and compares both Sparc KO and Sparc overexpression in male and female mice. First, I explore the consequences of Sparc KO and compare them to the ageing phenotype. I also observe the effects of exercise. In the second part, I study the consequences of SPARC overexpression and compare them to the exercise benefits. The measurements were mainly related to tissue weights, adiposity, metabolism, and muscle strength. Collectively, these findings and data show that Sparc KO mice manifest an ageing-like phenotype, whereas SPARC overexpression and exercise generate similar benefits. These benefits are towards counteracting both SPARC deficiency-induced ageing-like phenotype as well as reversing the age-related changes. The potential applications of these findings are to build/optimize Sparc KO-based animal models of various health conditions and, on the other hand, to develop therapies based on introducing SPARC or targeting SPARC-related pathways to mimic exercise against obesity, age-related and metabolic disorders. Exploring such molecular patterns would allow both mapping some underlying mechanisms and developing a new generation of therapeutic options for obesity and metabolic disorders, including age-related disorders. Such approaches would be based on targeting TFF2, SPARC or their related pathways

Enhanced Living Environments

This open access book was prepared as a Final Publication of the COST Action IC1303 “Algorithms, Architectures and Platforms for Enhanced Living Environments (AAPELE)”. The concept of Enhanced Living Environments (ELE) refers to the area of Ambient Assisted Living (AAL) that is more related with Information and Communication Technologies (ICT). Effective ELE solutions require appropriate ICT algorithms, architectures, platforms, and systems, having in view the advance of science and technology in this area and the development of new and innovative solutions that can provide improvements in the quality of life for people in their homes and can reduce the financial burden on the budgets of the healthcare providers. The aim of this book is to become a state-of-the-art reference, discussing progress made, as well as prompting future directions on theories, practices, standards, and strategies related to the ELE area. The book contains 12 chapters and can serve as a valuable reference for undergraduate students, post-graduate students, educators, faculty members, researchers, engineers, medical doctors, healthcare organizations, insurance companies, and research strategists working in this area

1997-1999-UNM CATALOG

Course catalog for 1997-1999https://digitalrepository.unm.edu/course_catalogs/1097/thumbnail.jp