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Oxygen Levels: Normal, Low, And High Levels Explained
By Javier Andreu-Perez 1, 2, Luis Garcia-Gancedo 3, Jonathan McKinnell 4, Anniek Van der Drift 5, Adam Powell 5, Valentin Hamy 3, Thomas Keller 4 and Guang-Zhong Yang 1, *
In addition to routine clinical examination, unobtrusive and physical monitoring of Rheumatoid Arthritis (RA) patients provides an important source of information to enable understanding the impact of the disease on quality of life. Besides an increase in sedentary behaviour, pain in RA can negatively impact simple physical activities such as getting out of bed and standing up from a chair. The objective of this work is to develop a method that can generate fine-grained actigraphies to capture the impact of the disease on the daily activities of patients. A processing methodology is presented to automatically tag activity accelerometer data from a cohort of moderate-to-severe RA patients. A study of procesing methods based on machine learning and deep learning is provided. Thirty subjects, 10 RA patients and 20 healthy control subjects, were recruited in the study. A single tri-axial accelerometer was attached to the position of the fifth lumbar vertebra (L5) of each subject with a tag prediction granularity of 3 s. The proposed method is capable of handling unbalanced datasets from tagged data while accounting for long-duration activities such as sitting and lying, as well as short transitions such as sit-to-stand or lying-to-sit. The methodology also includes a novel mechanism for automatically applying a threshold to predictions by their confidence levels, in addition to a logical filter to correct for infeasible sequences of activities. Performance tests showed that the method was able to achieve around 95% accuracy and 81% F-score. The produced actigraphies can be helpful to generate objective RA disease-specific markers of patient mobility in-between clinical site visits.
Rheumatoid arthritis (RA) is a chronic systemic disease that typically affects adults between the ages of 30 and 60 [1], leading to a life disability in which patients describe increasing pain in several joints over time. This disease has a prevalence of 0.24% that varies between 0.3% and 1% in the developed countries [2, 3]. Some studies have also concluded that the risk of mortality of RA patients is approximately 38% higher than the general population, with an increased risk of 55% for women [4]. RA patients are likely to exhibit worse functional status and quality of life than those affected by osteoarthritis [5, 6]. A high percentage of RA sufferers (around 50%) are unable to hold a full time job due to the considerable impact of the disease on their daily lives [3]. Research also suggests that the decrease in physical function correlates with a high prevalence of depression and anxiety [7, 8]. Thus far, the effect of the disease is assessed based on sparse time-point check-outs and questionnaires during ordinary appointments.
Pdf) Autonomous Motivation To Reduce Sedentary Behaviour Is Associated With Less Sedentary Time And Improved Health Outcomes In Rheumatoid Arthritis: A Longitudinal Study
Recent advances in wearable sensing allow continuous patient monitoring in an unobtrusive way, providing a potential source for estimating actigraphies [9, 10, 11]. The use of additional motion sensors such as gyroscopes, magnetometers and inertial measurement units provides a rich source of kinematic information to obtain effortless actigraphies with high precision and accuracy [12, 13]. Clinical studies using several accelerometers have been proposed for studies lasting from one to three days [14]. However, due to battery, life long-term continuous clinical mobility experiments, performed during periods of several weeks up to months, have employed of single inertial sensors with on-board storage memory [15]. Consequently, in the present study, inertial data is also collected from a single tri-axial accelerometer attached to the skin in the position of the fifth lumbar vertebra (L5). The effects of RA can lead to pain in different parts of the body therefore limiting the range of movement, as well as articular sensitivity and function. Hence, certain patients may exhibit distorted mobility patterns, which can make the recognition of a determined activity from a signal segment difficult.
Actigraphies provide a closer look at some relevant factors in RA management such as alteration on patient life-styles and changes on their circadian rhythms [16, 17, 18, 19]. However, these factors cannot be monitored accurately by ordinary appointments that are sparse in time by nature. Manual actigraphies are prone to subjectivity and ambiguity, hence the importance of automated methods for continuous assessment. Fine-grained actigraphies able to predict physical activities from accelerometer signals are key to elucidate novel digital endpoints. In this work a laboratory study is performed on rheumatoid arthritis patients to evaluate the capabilities of the recordings from a single sensor to recognize a set of human physical activities. A set of desirable activity tags are defined, namely sedentary activities (sitting, lying, and standing), acyclic activities (walking), short-timed transitions (sitting-to-standing, standing-to-sitting, lying-to-sitting, sitting-to-lying), and composite activities (lying-to-standing, standing-to-lying). In order to predict these activity tags over the acceleration data from a single sensor, a “hierarchical dichotomy” is proposed and described in detail. Several alternative machine learning methods are used and performance results are compared. Additional functions to filter for unknown activities as well as a logical filter to correct for an unfeasible sequence of transitions are also introduced.
While questionnaires and self-administered surveys of behaviours, mobility and quality of life have been largely used in clinical research in RA, their susceptibility to error and subjectivity have stimulated the utilisation of wearable sensors [20]. A physical activity assessment with more than a hundred RA patients has been attempted with an accelerometer attached to the waistline of the right hip in line with the right axila [21]. The accelerometer was used to count activity bouts occurring in a ten-minute sequence. Activity bouts can be used as a surrogate measure of the relative amount of activity; however, it does require long periods of recording and the specific activity the patient is performing is undetermined. In [22], a study with a cohort of 98 RA patients used an accelerometer sensor attached to the arm to determine an actigraphy based on the detection of four different levels of activity (sedentary, very light, light and moderate) based on a scale related to energy expenditure. The time that a patient spends performing each activity level is used as a measure of clinical interest. Although activity levels provide an insight into the amount of physical activity that patients perform during the day, a higher level of detail on the actigraphy could provide additional information on the effects that disease manifestation might have on patient behaviours, circadian rhythms and daily activity patterns.
Is Your Medicine Cabinet Winter Ready?
Generating actigraphies from RA patients is challenging as they may exhibit an amalgam of anomalous movement patterns. Issues in mobility have been described in RA patients for different activities such as those arising from a chair or bed as a result of poor gripping and trunk flexion, low range of movement and pain [23, 24] or difficulty walking due to weak and painful knees [23], disturbance during sitting or lying due to stiffness and hip pain [25], and difficulty of maintaining a standing upright position [25]. As a difference to healthy subjects who might exhibit a quite similar kinetic and kinematic pattern when performing activities and transitions, disorders in activity patterns can be presented at different levels of intensity with respect to the level of the disease. Most common symptoms are displayed in Figure 1. Moreover, symptoms vary across patients and such heterogenity may accentuate the differences between patterns even further. The key challenge that this paper aims to address is to provide a method that can produce accurate fine-grained actigraphies from RA patients with moderate to severe levels of disease. In this paper, we present a method that have worked effectively in a clinical setting.
A mobility lab experiment was performed at GSK’s clinical unit in Addenbrookes Hospital (Cambridge, UK), where ten patients with moderate to severe RA and twenty healthy volunteers (HV) were asked to perform a circuit of daily living activities. This consisted of ten tasks denoted as sitting, lying, standing, walking and the transitions sit-to-stand, stand-to-sit, lie-to-sit, sit-to-lie, lie-to-stand and stand-to-lie. The tasks were performed according to a circuit that
Generating actigraphies from RA patients is challenging as they may exhibit an amalgam of anomalous movement patterns. Issues in mobility have been described in RA patients for different activities such as those arising from a chair or bed as a result of poor gripping and trunk flexion, low range of movement and pain [23, 24] or difficulty walking due to weak and painful knees [23], disturbance during sitting or lying due to stiffness and hip pain [25], and difficulty of maintaining a standing upright position [25]. As a difference to healthy subjects who might exhibit a quite similar kinetic and kinematic pattern when performing activities and transitions, disorders in activity patterns can be presented at different levels of intensity with respect to the level of the disease. Most common symptoms are displayed in Figure 1. Moreover, symptoms vary across patients and such heterogenity may accentuate the differences between patterns even further. The key challenge that this paper aims to address is to provide a method that can produce accurate fine-grained actigraphies from RA patients with moderate to severe levels of disease. In this paper, we present a method that have worked effectively in a clinical setting.
A mobility lab experiment was performed at GSK’s clinical unit in Addenbrookes Hospital (Cambridge, UK), where ten patients with moderate to severe RA and twenty healthy volunteers (HV) were asked to perform a circuit of daily living activities. This consisted of ten tasks denoted as sitting, lying, standing, walking and the transitions sit-to-stand, stand-to-sit, lie-to-sit, sit-to-lie, lie-to-stand and stand-to-lie. The tasks were performed according to a circuit that
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