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      Digital Health: Tracking Physiomes and Activity Using Wearable Biosensors Reveals Useful Health-Related Information

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          Abstract

          A new wave of portable biosensors allows frequent measurement of health-related physiology. We investigated the use of these devices to monitor human physiological changes during various activities and their role in managing health and diagnosing and analyzing disease. By recording over 250,000 daily measurements for up to 43 individuals, we found personalized circadian differences in physiological parameters, replicating previous physiological findings. Interestingly, we found striking changes in particular environments, such as airline flights (decreased peripheral capillary oxygen saturation [SpO 2] and increased radiation exposure). These events are associated with physiological macro-phenotypes such as fatigue, providing a strong association between reduced pressure/oxygen and fatigue on high-altitude flights. Importantly, we combined biosensor information with frequent medical measurements and made two important observations: First, wearable devices were useful in identification of early signs of Lyme disease and inflammatory responses; we used this information to develop a personalized, activity-based normalization framework to identify abnormal physiological signals from longitudinal data for facile disease detection. Second, wearables distinguish physiological differences between insulin-sensitive and -resistant individuals. Overall, these results indicate that portable biosensors provide useful information for monitoring personal activities and physiology and are likely to play an important role in managing health and enabling affordable health care access to groups traditionally limited by socioeconomic class or remote geography.

          Author Summary

          A new wave of wearable sensors allows frequent and continuous measurements of body functions (physiology), including heart rate, skin temperature, blood oxygen levels, and physical activity. We investigated the ability of wearable sensors to follow physiological changes that occur over the course of a day, during illness and other activities. Data from these sensors revealed personalized differences in daily patterns of activities. Interestingly, we discovered striking changes in particular environments such as airline flights. Blood oxygen levels decreased during high-altitude flights, and this decrease was associated with fatigue. By combining sensor information with frequent medical measurements, we made two important health-related observations. First, wearable sensors were useful in identifying the onset of Lyme disease and inflammation. From this observation, we then developed a computational algorithm for personalized disease detection using such sensors. Second, we found that wearable sensors can reveal physiological differences between insulin-sensitive and insulin-resistant individuals, raising the possibility that these sensors could help detect risk for type 2 diabetes. Overall, these results indicate that the information provided by wearable sensors is physiologically meaningful and actionable. Wearable sensors are likely to play an important role in managing health.

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          Agreement between two methods of clinical measurement can be quantified using the differences between observations made using the two methods on the same subjects. The 95% limits of agreement, estimated by mean difference +/- 1.96 standard deviation of the differences, provide an interval within which 95% of differences between measurements by the two methods are expected to lie. We describe how graphical methods can be used to investigate the assumptions of the method and we also give confidence intervals. We extend the basic approach to data where there is a relationship between difference and magnitude, both with a simple logarithmic transformation approach and a new, more general, regression approach. We discuss the importance of the repeatability of each method separately and compare an estimate of this to the limits of agreement. We extend the limits of agreement approach to data with repeated measurements, proposing new estimates for equal numbers of replicates by each method on each subject, for unequal numbers of replicates, and for replicated data collected in pairs, where the underlying value of the quantity being measured is changing. Finally, we describe a nonparametric approach to comparing methods.
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            Individual differences in response to regular physical activity.

            The purpose of this review was to address the question of interindividual variation in responsiveness to regular exercise training and to define the contributions of age, sex, race, and pretraining phenotype level to this variability. A literature review was conducted of the studies reporting interindividual variation in responsiveness to standardized and controlled exercise-training programs, and included an analysis of the contribution of age, sex, race, and initial phenotype values to the heterogeneity in VO(2max), high-density lipoprotein (HDL)-C and submaximal exercise, heart rate (HR), and systolic blood pressure (SBP) training responses in subjects from the HERITAGE Family Study. Several studies have shown marked individual differences in responsiveness to exercise training. For example, VO(2max) responses to standardized training programs have ranged from almost no gain up to 100% increase in large groups of sedentary individuals. A similar pattern of heterogeneity has been observed for other phenotypes. Data from the HERITAGE Family Study show that age, sex, and race have little impact on interindividual differences in training responses. On the other hand, the initial level of a phenotype is a major determinant of training response for some traits, such as submaximal exercise heart rate and blood pressure (BP) but has only a minor effect on others (e.g., VO(2max), HDL-C). The contribution of familial factors (shared environment and genetic factors) is supported by data on significant familial aggregation of training response phenotypes. There is strong evidence for considerable heterogeneity in the responsiveness to regular physical activity. Age, sex, and ethnic origin are not major determinants of human responses to regular physical activity, whereas the pretraining level of a phenotype has a considerable impact in some cases. Familial factors also contribute significantly to variability in training response.
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              Is Open Access

              Hypoxemia in patients with COPD: cause, effects, and disease progression

              Chronic obstructive pulmonary disease (COPD) is a leading cause of death and disability internationally. Alveolar hypoxia and consequent hypoxemia increase in prevalence as disease severity increases. Ventilation/perfusion mismatch resulting from progressive airflow limitation and emphysema is the key driver of this hypoxia, which may be exacerbated by sleep and exercise. Uncorrected chronic hypoxemia is associated with the development of adverse sequelae of COPD, including pulmonary hypertension, secondary polycythemia, systemic inflammation, and skeletal muscle dysfunction. A combination of these factors leads to diminished quality of life, reduced exercise tolerance, increased risk of cardiovascular morbidity, and greater risk of death. Concomitant sleep-disordered breathing may place a small but significant subset of COPD patients at increased risk of these complications. Long-term oxygen therapy has been shown to improve pulmonary hemodynamics, reduce erythrocytosis, and improve survival in selected patients with severe hypoxemic respiratory failure. However, the optimal treatment for patients with exertional oxyhemoglobin desaturation, isolated nocturnal hypoxemia, or mild-to-moderate resting daytime hypoxemia remains uncertain.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Project administrationRole: SoftwareRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Project administrationRole: SoftwareRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Project administrationRole: ResourcesRole: ValidationRole: VisualizationRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: SoftwareRole: Visualization
                Role: ConceptualizationRole: Project administrationRole: Resources
                Role: ConceptualizationRole: Formal analysisRole: InvestigationRole: MethodologyRole: SoftwareRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Project administrationRole: ResourcesRole: Writing – review & editing
                Role: Resources
                Role: Formal analysisRole: InvestigationRole: SoftwareRole: Visualization
                Role: Resources
                Role: Formal analysisRole: InvestigationRole: SoftwareRole: Visualization
                Role: Data curation
                Role: Resources
                Role: ConceptualizationRole: Funding acquisitionRole: Project administrationRole: SupervisionRole: Writing – original draftRole: Writing – review & editing
                Role: Academic Editor
                Journal
                PLoS Biol
                PLoS Biol
                plos
                plosbiol
                PLoS Biology
                Public Library of Science (San Francisco, CA USA )
                1544-9173
                1545-7885
                12 January 2017
                January 2017
                12 January 2017
                : 15
                : 1
                : e2001402
                Affiliations
                [1 ]Department of Genetics, Stanford University School of Medicine, Stanford, California, United States of America
                [2 ]Mobilize Center, Stanford University, Palo Alto, California, United States of America
                [3 ]Spinal Cord Injury Service, Veterans Affairs Palo Alto Health Care System, Palo Alto, California, United States of America
                [4 ]Department of Neurosurgery, Stanford University School of Medicine, Stanford, California, United States of America
                [5 ]Division of Endocrinology, Stanford University School of Medicine, Stanford, California, United States of America
                Newcastle University, United Kingdom of Great Britain and Northern Ireland
                Author notes

                I have read the journal's policy and the authors of this manuscript have the following competing interests. A patent based on the disease detection work is in preparation.

                Article
                pbio.2001402
                10.1371/journal.pbio.2001402
                5230763
                28081144
                e7475d70-0912-4068-8d8c-46c1567656e0

                This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

                History
                : 23 October 2016
                : 5 December 2016
                Page count
                Figures: 7, Tables: 0, Pages: 30
                Funding
                NIH (grant number UL1 TR001085). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. NIH https://datascience.nih.gov/bd2k (grant number U54 EB020405). JD is funded by the Mobilize Center grant. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The Stanford Big Data Initiative. Received by MPS. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Bert and Candace Forbes. Received by MPS. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Department of Veterans Affairs Office of Academic Affiliations Advanced Fellowship in Spinal Cord Injury Medicine https://www.va.gov/oaa/specialfellows/programs/SF_SCIMinfo.asp?p=16. Received by SMS-FR. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. NIH (grant number 8U54DK102556). Received by MPS. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Physiology
                Physiological Parameters
                Body Temperature
                Skin Temperature
                Medicine and Health Sciences
                Physiology
                Physiological Parameters
                Body Temperature
                Skin Temperature
                Engineering and Technology
                Aerospace Engineering
                Flight Testing
                Engineering and Technology
                Equipment
                Measurement Equipment
                Biology and Life Sciences
                Physiology
                Physiological Parameters
                Medicine and Health Sciences
                Physiology
                Physiological Parameters
                Biology and Life Sciences
                Physiology
                Sensory Physiology
                Medicine and Health Sciences
                Physiology
                Sensory Physiology
                Biology and Life Sciences
                Biotechnology
                Medical Devices and Equipment
                Medicine and Health Sciences
                Medical Devices and Equipment
                Medicine and Health Sciences
                Infectious Diseases
                Bacterial Diseases
                Borrelia Infection
                Lyme Disease
                Medicine and Health Sciences
                Rheumatology
                Lyme Disease
                Medicine and Health Sciences
                Inflammatory Diseases
                Custom metadata
                These data are available at http://hmpdacc.org/data/wearable/stanford.tar.

                Life sciences
                Life sciences

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