Exploring Sedentary Behavior as a Secondary Prevention Target for Heart Disease

The purpose of this dissertation series was to describe sedentary behavior and its associations with cardiovascular disease (CVD) biomarkers and outcomes, and to explore the potential that reducing sedentary behavior may be a secondary prevention target for Acute Coronary Syndrome (ACS) survivors. As such, the following series of research studies evaluate the mechanisms, patterns, and correlates of sedentary behavior in relation to CVD risk and examine whether sedentary behavior might be a risk factor for CVD outcomes among ACS survivors. In Chapter II, a cross-sectional study of young, healthy adults examined a set of biomarkers representing several aspects of endothelial cell health to elucidate the relationship between free-living, habitual sedentary time and endothelial dysfunction. Results showed that there were no differences in measures of endothelial cell injury, endothelial cell reparative capacity, or upper extremity endothelium-dependent vasodilatation in participants with high compared with low volumes of device-measured sedentary behavior in a sample of young, healthy adults. These findings suggest that physiological mechanisms other than endothelial dysfunction may need to be explored as a potential link between habitual prolonged sedentary time and CVD in young adults. Chapter III employed group-based trajectory modeling to identify distinct patterns of sedentary behavior, as measured by accelerometry, in ACS survivors over the 28 consecutive days following hospital discharge, and, secondly, to explore potential correlates of these patterns. Results demonstrated that ACS patients as a group engaged in high volumes of accelerometer-measured sedentary time. Three patterns of sedentary behavior over the first month post-discharge were identified; these involved either gradual or rapid reductions in sedentary behavior. Several measures of disease severity and physical health (e.g., GRACE CVD risk score, physical health-related quality of life), and partner status (i.e., married or partnered or without partner), were associated with the worst patterns of sedentary behavior (i.e., high volume of sedentary time with only a slight decline over time). These findings provide insight on the different patterns of sedentary behavior that emerge as patients resume their daily life over the first month post hospital discharge. Chapter IV, building upon the study presented in Chapter III, examined whether accelerometer-measured sedentary behavior of ACS survivors over the first month post hospital discharge was associated with 1-year health outcomes. The purpose of this study was to understand whether sedentary behavior in the early post hospital discharge period may be an important risk factor in ACS survivors, that might be targeted in secondary prevention strategies. Results demonstrated that the average sedentary behavior over the first month post hospital discharge was not significantly associated with increased risk of 1-year recurrent major adverse cardiovascular events or hospitalizations. These findings do not support sedentary behavior in the early post hospital discharge period as a prognostic risk factor that should be modified in ACS survivors as part of secondary heart disease prevention strategy. However, studies with larger sample sizes, and that evaluate sedentary behavior patterns beyond the first month are needed. Collectively, these studies show that high volumes of sedentary behavior are prevalent in ACS survivors over the first month immediately following hospital discharge. Future work is needed to further study the underlying mechanisms through which sedentary behavior may confer CVD risk and to determine whether sedentary behavior is an important modifiable risk factor in ACS survivors

Activity behavior and physiological profile of advanced-stage ovarian cancer survivors

Background: Advanced-stage ovarian cancer survivors (OCS) often experience a multitude of disease symptoms and treatment-related side-effects. Additionally, most OCS are older, have comorbidities, are overweight or obese, and report being insufficiently physically active. Ovarian cancer survivors may benefit from exercise oncology interventions to reduce symptom-burden, manage comorbidities, minimize functional decline and maximize health-related quality of life (HRQoL). However, current knowledge gaps regarding the physiological characteristics of OCS throughout the entire survivorship spectrum challenge the development of tailored exercise interventions. Purpose: The overall purpose of this thesis was to provide a more comprehensive physiological and activity behavior profile of post-treatment advanced-stage OCS. Specifically, a cross-sectional research study was conducted to compare objectively measured activity behavior and physical function, body composition and musculoskeletal morphology, self-reported pelvic floor dysfunction (PFD) and HRQoL of OCS with age-matched controls. Associations between activity behavior, physiological characteristics, PFD and HRQoL for OCS were also investigated. Methods: Twenty stage III-IV OCS and 20 age-matched controls underwent objective assessments of activity behavior (physical activity and sedentary time via 7-day accelerometry), physical function (400-meter walk to assess cardiorespiratory fitness, repeated chair rise to assess lower extremity function, 6-meter walking tests to assess gait speed and dynamic balance), muscle strength (1-repetition maximum chest press and single leg extension, and handgrip strength), body composition (dual-energy x-ray absorptiometry) and musculoskeletal morphology (peripheral quantitative computed tomography), and completed questionnaires assessing HRQoL (SF-36) and PFD (Australian Pelvic Floor Questionnaire). Results: Compared to controls, OCS spent more time/day in prolonged sedentary bouts (i.e., uninterrupted sedentary bouts of ≥30 min; p = 0.039), had lower cardiorespiratory fitness (p =0.041) and upper body strength (p = 0.023), had higher areal bone mineral content (p = 0.047) and volumetric trabecular density (p = 0.048), but were not different in other measures of body composition or musculoskeletal morphology (i.e., all p-values \u3e 0.050). Compared to controls, OCS had equivalent self-reported PFD as indicated by combined bladder, bowel and pelvic organ prolapse symptoms (p = 0.277), but worse physical HRQoL indicated by a physical composite score (p = 0.013). Only 20% (n = 4) of OCS accrued ≥150 minutes/week moderate-and-vigorous physical activity (MVPA) in ≥10 min bouts. MVPA time/day in ≥10 min bouts was positively associated with cardiorespiratory fitness (p = 0.001), lower extremity function, (p = 0.019), muscle crosssectional area (p = 0.035), less PFD (p = 0.038) and physical HRQoL (p = 0.003). Decreased physical HRQoL was associated with less MVPA (p = 0.005), more sedentary time (p = 0.047), decreased objective physical function (p-values \u3c 0.050) and greater PFD (p = 0.043). Conclusion: Post-treatment advanced-stage OCS spent more time in prolonged sedentary bouts, had lower cardiorespiratory fitness, upper body strength and physical HRQoL compared to agematched controls. The decreased physical HRQoL of this sample of OCS compared to controls and its associations with modifiable factors such as MVPA, sedentary time, objective physical function and PFD highlights the need for ongoing supportive care and the importance of multidisciplinary interventions, including exercise oncology interventions, beyond the completion of first-line ovarian cancer treatment

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

Wearables, smartphones, and artificial intelligence for digital phenotyping and health

Ubiquitous progress in wearable sensing and mobile computing technologies, alongside growing diversity in sensor modalities, has created new pathways for the collection of health and well-being data outside of laboratory settings, in a longitudinal fashion. Wearable and mobile devices have the potential to provide low-cost, objective measures of physical activity, clinically relevant data for patient assessment, and scalable behavior monitoring in large populations. These data can be used in both interventional and observational studies to derive insights regarding the links between behavior, health. and disease, as well as to advance the personalization and effectiveness of commercial wellness applications. Today, over 400,000 participants have had their behavior tracked prospectively using accelerometers for epidemiological studies across the globe. Traditionally, epidemiologists and clinicians have relied upon self-report measures of physical activity and sleep which, while valuable in the absence of alternatives, are subject to bias and often provide partial, incomplete information Physical behavior data extracted from wearable devices are being used to derive sensor-assessed, objective measures of physical behaviors, overcoming the limitations of self-report with the aim of relating these to clinical endpoints and eventually applying the findings to preventive and predictive medicine. Moreover, the application of artificial intelligence (AI), sensor fusion, and signal processing to wearable sensor data has led to improved human activity recognition and behavioral phenotyping. Here, we review the state of the art in wearable and mobile sensing technology in epidemiology and clinical medicine and discuss how AI is changing the field

Automatic Generation of Personalized Recommendations in eCoaching

Denne avhandlingen omhandler eCoaching for personlig livsstilsstøtte i sanntid ved bruk av informasjons- og kommunikasjonsteknologi. Utfordringen er å designe, utvikle og teknisk evaluere en prototyp av en intelligent eCoach som automatisk genererer personlige og evidensbaserte anbefalinger til en bedre livsstil. Den utviklede løsningen er fokusert på forbedring av fysisk aktivitet. Prototypen bruker bærbare medisinske aktivitetssensorer. De innsamlede data blir semantisk representert og kunstig intelligente algoritmer genererer automatisk meningsfulle, personlige og kontekstbaserte anbefalinger for mindre stillesittende tid. Oppgaven bruker den veletablerte designvitenskapelige forskningsmetodikken for å utvikle teoretiske grunnlag og praktiske implementeringer. Samlet sett fokuserer denne forskningen på teknologisk verifisering snarere enn klinisk evaluering.publishedVersio

The use of data mining methods for the prediction of dementia : evidence from the English longitudinal study of aging

Dementia in older age is a major health concern with the increase in the aging population. Preventive measures to prevent or delay dementia symptoms are of utmost importance. In this study, a large and wide variety of factors from multiple domains were investigated using a large nationally-representative sample of older people from the English Longitudinal Study of Ageing (ELSA). Seven machine learning algorithms were implemented to build predictive models for performance comparison. A simple model ensemble approach was used to combine the prediction results of individual base models to further improve predictive power. A series of important factors in each domain area were identified. The findings from this study provide new evidence on factors that are associated with the dementia in later life. This information will help our understanding of potential risk factors for dementia and identify warning signs of the early stages of dementia. Longitudinal research is required to establish which factors may be causative and which factors may be a consequence of dementia

Detecting Periods of Eating in Everyday Life by Tracking Wrist Motion — What is a Meal?

Eating is one of the most basic activities observed in sentient animals, a behavior so natural that humans often eating without giving the activity a second thought. Unfortunately, this often leads to consuming more calories than expended, which can cause weight gain - a leading cause of diseases and death. This proposal describes research in methods to automatically detect periods of eating by tracking wrist motion so that calorie consumption can be tracked. We first briefly discuss how obesity is caused due to an imbalance in calorie intake and expenditure. Calorie consumption and expenditure can be tracked manually using tools like paper diaries, however it is well known that human bias can affect the accuracy of such tracking. Researchers in the upcoming field of automated dietary monitoring (ADM) are attempting to track diet using electronic methods in an effort to mitigate this bias. We attempt to replicate a previous algorithm that detects eating by tracking wrist motion electronically. The previous algorithm was evaluated on data collected from 43 subjects using an iPhone as the sensor. Periods of time are segmented first, and then classified using a naive Bayesian classifier. For replication, we describe the collection of the Clemson all-day data set (CAD), a free-living eating activity dataset containing 4,680 hours of wrist motion collected from 351 participants - the largest of its kind known to us. We learn that while different sensors are available to log wrist acceleration data, no unified convention exists, and this data must thus be transformed between conventions. We learn that the performance of the eating detection algorithm is affected due to changes in the sensors used to track wrist motion, increased variability in behavior due to a larger participant pool, and the ratio of eating to non-eating in the dataset. We learn that commercially available acceleration sensors contain noise in their reported readings which affects wrist tracking specifically due to the low magnitude of wrist acceleration. Commercial accelerometers can have noise up to 0.06g which is acceptable in applications like automobile crash testing or pedestrian indoor navigation, but not in ones using wrist motion. We quantify linear acceleration noise in our free-living dataset. We explain sources of noise, a method to mitigate it, and also evaluate the effect of this noise on the eating detection algorithm. By visualizing periods of eating in the collected dataset we learn that that people often conduct secondary activities while eating, such as walking, watching television, working, and doing household chores. These secondary activities cause wrist motions that obfuscate wrist motions associated with eating, which increases the difficulty of detecting periods of eating (meals). Subjects reported conducting secondary activities in 72% of meals. Analysis of wrist motion data revealed that the wrist was resting 12.8% of the time during self-reported meals, compared to only 6.8% of the time in a cafeteria dataset. Walking motion was found during 5.5% of the time during meals in free-living, compared to 0% in the cafeteria. Augmenting an eating detection classifier to include walking and resting detection improved the average per person accuracy from 74% to 77% on our free-living dataset (t[353]=7.86, p\u3c0.001). This suggests that future data collections for eating activity detection should also collect detailed ground truth on secondary activities being conducted during eating. Finally, learning from this data collection, we describe a convolutional neural network (CNN) to detect periods of eating by tracking wrist motion during everyday life. Eating uses hand-to-mouth gestures for ingestion, each of which lasts appx 1-5 sec. The novelty of our new approach is that we analyze a much longer window (0.5-15 min) that can contain other gestures related to eating, such as cutting or manipulating food, preparing foods for consumption, and resting between ingestion events. The context of these other gestures can improve the detection of periods of eating. We found that accuracy at detecting eating increased by 15% in longer windows compared to shorter windows. Overall results on CAD were 89% detection of meals with 1.7 false positives for every true positive (FP/TP), and a time weighted accuracy of 80%

Behavioral Privacy Risks and Mitigation Approaches in Sharing of Wearable Inertial Sensor Data

Wrist-worn inertial sensors in activity trackers and smartwatches are increasingly being used for daily tracking of activity and sleep. Wearable devices, with their onboard sensors, provide appealing mobile health (mHealth) platform that can be leveraged for continuous and unobtrusive monitoring of an individual in their daily life. As a result, an adaptation of wrist-worn devices in many applications (such as health, sport, and recreation) increases. Additionally, an increasing number of sensory datasets consisting of motion sensor data from wrist-worn devices are becoming publicly available for research. However, releasing or sharing these wearable sensor data creates serious privacy concerns of the user. First, in many application domains (such as mHealth, insurance, and health provider), user identity is an integral part of the shared data. In such settings, instead of identity privacy preservation, the focus is more on the behavioral privacy problem that is the disclosure of sensitive behaviors from the shared sensor data. Second, different datasets usually focus on only a select subset of these behaviors. But, in the event that users can be re-identified from accelerometry data, different databases of motion data (contributed by the same user) can be linked, resulting in the revelation of sensitive behaviors or health diagnoses of a user that was neither originally declared by a data collector nor consented by the user. The contributions of this dissertation are multifold. First, to show the behavioral privacy risk in sharing the raw sensor, this dissertation presents a detailed case study of detecting cigarette smoking in the field. It proposes a new machine learning model, called puffMarker, that achieves a false positive rate of 1/6 (or 0.17) per day, with a recall rate of 87.5%, when tested in a field study with 61 newly abstinent daily smokers. Second, it proposes a model-based data substitution mechanism, namely mSieve, to protect behavioral privacy. It evaluates the efficacy of the scheme using 660 hours of raw sensor data collected and demonstrates that it is possible to retain meaningful utility, in terms of inference accuracy (90%), while simultaneously preserving the privacy of sensitive behaviors. Finally, it analyzes the risks of user re-identification from wrist-worn sensor data, even after applying mSieve for protecting behavioral privacy. It presents a deep learning architecture that can identify unique micro-movement pattern in each wearer\u27s wrists. A new consistency-distinction loss function is proposed to train the deep learning model for open set learning so as to maximize re-identification consistency for known users and amplify distinction with any unknown user. In 10 weeks of daily sensor wearing by 353 participants, we show that a known user can be re-identified with a 99.7% true matching rate while keeping the false acceptance rate to 0.1% for an unknown user. Finally, for mitigation, we show that injecting even a low level of Laplace noise in the data stream can limit the re-identification risk. This dissertation creates new research opportunities on understanding and mitigating risks and ethical challenges associated with behavioral privacy