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1. Introduction
The gravitational force on Earth has remained constant in direction and magnitude since the formation of the planet [1]. Therefore, living species including plants, animals, and humans have evolved to cope with and rely upon gravity equal to 1 g. Throughout the history of the Earth, all living organisms adapted their cellular and behavioral function to this particular physical environment characteristic for our home planet. Gravity—as a permanent and constant vector-calibrated stimulus—led to various gravity-perceiving systems in organisms that control growth or influence movement and behavior. But what happens if this constant stimulus is changed?
Future challenges in terms of long-term interplanetary manned space missions moved the adaptability of living organisms and their vital systems to heterogravitational habitats into scientific focus [2]. With emphasis on our astronomical neighbors Mars and Moon with a reduced gravitational force of approximately a third and a fifth of the Earth’s gravitation, it became apparent that orbital or interplanetary space explorations require knowledge about gravity-perceiving systems, which determine movement, cognition, and survival [3]. In the past decades, space research manifested a significant gravity dependency for various biological processes and vital systems. A special focus lies on the animal and human nervous system (NS) as it is crucial for integration of sensory input, for example, from the vestibular system, movement control, and terrestrial locomotion on Earth. The NS governs muscle contraction enabling the body to counteract the gravitational force and controlling locomotor patterns and reflexes during the evolutionary shift from aqueous to terrestrial life. For interplanetary and orbital missions in future human space flight, knowledge about the gravity sensitivity of the NS is crucial to anticipate major challenges, train the astronauts, and prepare adequate countermeasures to conserve elementary sensorimotor skills during long-term partial-gravity exposure.
The NS is a network of neurons and fibers which transmits nerve impulses between parts of the body. It is composed of interconnected nerve and supporting glial cells. The mechanism of neuronal communication is based on electrochemical coupling, the modulation of intra- and extracellular ions to modify the electrical properties of a cell (intracellular signaling), and the controlled release of transmitters (intercellular communication). Resulting action potentials (APs) are the basic communication unit, and their conduction frequency serves as the coding for the stimulus’ intensity.
One of the fundamental circuits within the central nervous system (CNS) to control muscle contraction is the monosynaptic reflex arch [4]. These reflexes are neuromuscular reactions in response to an external stimulus, which lead to fast muscle contractions. The magnitude of muscle contraction depends on the magnitude of sensory input. Allowing mobility of terrestrial life, sensory input from the vestibular and visual systems and proprioception is processed by the NS, and by means of muscle innervation, appropriate forces are generated to control simple posture or movement [5, 6, 7, 8, 9, 10]. These sensorimotor competencies are crucial for life. Since the first manned spaceflight of Yuri Gagarin in 1961, the effect of microgravity on the human body has been intensively investigated. In the decades since his first spaceflight, many experiments have been performed which made gravity-induced changes on astronauts and cosmonauts apparent. With an emphasis on weightlessness and our astronomical neighbors Mars and Moon [2, 5], the authors found directly related health effects, among others a persistent modulation in the sensory [7, 11] and motor system [12] and the resulting structural loss of muscle [13] and bone mass [14]. In addition, there are modulations in the neuromuscular system underlying those health-related changes that open up many questions on how the variation of gravity influences the NS. These questions led to numerous experiments to investigate the effect of varying gravity conditions on the different levels of organization, from the molecular and cellular level, up to the whole NS and its interconnection with movement control and mobility. The functional properties of these levels were thoroughly investigated, however, with barely any interconnection.
This chapter systematically reviews results on how changes in gravity affect neurons of human and animal as well as temporal and spatial characteristics of complex sensorimotor responses. For that purpose, the subject of this chapter is divided in three subthemes: the gravity dependence of subcellular and cellular parameters associated with neuronal activation is followed by an outline of the sensitivity of the human NS to gravitational variation in the context of movement. To interconnect these transdisciplinary findings, a working model is introduced on how the effects observed on the molecular and biophysical level may impact the sensorimotor control of the NS. The chapter ends with a conclusive statement that refers to movement in terms of long-term interplanetary manned space missions.
2. Gravity and the nervous system
2.1. The gravity dependence of subcellular and cellular parameters
A variety of life science experiments executed in gravity conditions different from Earth gravitation, 1 g, have been executed in cellular model systems. With an emphasize on subcellular and cellular parameters and the associated biophysical attributes, most of the in vitro experiments have been conducted on short-term gravity-research platforms as drop towers and parabolic flights. Findings from the late twentieth century and recent findings manifest a significant gravity dependency of the basic cell function associated with changes in membrane and channel properties as well as the underlying biophysical characteristics. Results are outlined in the following subchapter.
2.1.1. Membrane parameters
From experiments with unicellular organisms [15] and various cell types as immune cells [16] and neuronal cells [17], it is well established that single cells react to changes in gravity even though they do not have dedicated gravity-sensing structures. One of the major components that all these cell types and organisms have in common is the cell membrane. These complex structures are mainly composed of proteins and lipids [18].
To communicate, cells of the nervous system are able to modify their membrane potential. This ability is based on the activity of integrated membrane proteins as ion channels and ion pumps. But it is well known that the physicochemical state of the lipid membrane can directly modify the function of membrane proteins [19, 20]. In non-space-related experiments, it was shown that the closed-state probability of nicotinic acetylcholine receptors (nAChRs) increased with a decreased membrane fluidity [21]. These nAChRs are a major player in the sensorimotor system as they are located in the motor end plates that form the interface between the neuronal system and the muscles.
Due to these findings, experiments have been performed to monitor the changes of membrane viscosity in micro- and hypergravity with several models (artificial asolectin vesicles and human neuronal SH-SY5Y cells). In all models, the membrane fluidity significantly increases in microgravity and decreases in hypergravity, but in a different distinctness [22]. The difference in distinctness might be explained with the absence of a cytoskeleton in artificial membranes or a different lipid composition.
Nevertheless, this finding, that the membrane fluidity is gravity-dependent, will have a huge impact on biological and medical gravity research, as this is a basic physical mechanism that affects every cell in an organism [23].
2.1.2. Ion channel parameters
Ion channels are crucial for neuronal communication. They form controllable pores through the cell membrane. Charged ions can diffuse through these pores, following electrical and chemical gradients, changing the electrical properties of the cell. Ion channel parameters as open- and closed-state probability have been investigated by using pore-forming peptides which can be used as ion channel analogs. Until now, no native ion channel proteins have been used for gravity research.
The open-state probability of porin channels from Escherichia coli is significantly decreased in microgravity, whereas in hypergravity, it is increased. No effect on conductance was found [24].
Similar findings have been made with alamethicin, a pore-forming peptide from Trichoderma viride. In microgravity, the activity of alamethicin is decreased, whereas in hypergravity, it is increased [25, 26].
The effect on ion channels is—similar to changes in membrane fluidity—fully reversible and fast. With the onset of a different gravity condition, the open-state probability is changed, returning to normal as soon as the experiment returns to normal 1 g gravity.
2.1.3. Electrophysiological properties of single cells
By having a stable-resting potential, a cell is able to communicate. By changing the activity of relevant ion channels, the membrane potential can be modulated. During parabolic flight, the resting potential of human neuronal cells is significantly depolarized in microgravity and it is hyperpolarized in hypergravity. During microgravity, the depolarization is about 3 mV [27]. This gravity dependence of resting potential is not limited to excitable cells as neuronal cells; it was also found during a drop-tower mission in SF21 cells, an ovary cell line from the insect Spodoptera frugiperda [17].
Again in parabolic flight, in microgravity, the transmembrane currents in oocytes from Xenopus laevis show a significant decrease at a holding potential of −100 mV, whereas in hypergravity, there is a tendency of increased currents [28].
2.1.4. Propagation of action potentials
Action potentials (APs) are the basic communication unit in the nervous system. The intensity of a stimulus is frequency-coded: while the amplitude of APs remains constant, their frequency differs dependent on the stimulus strength. In microgravity obtained by drop tower, the rate of action potentials triggered by spontaneous active leech neurons is significantly increased [29]. This means on the level of single cells, more action potentials are generated in weightlessness.
Simultaneously, the conduction velocity of APs on the axonal level is decreased in microgravity and increased in hypergravity. This was demonstrated in parabolic flight missions in vitro in isolated earthworm axons and isolated rat axons and in vivo in intact earthworms. [29]. Again, the changes are fast and fully reversible.
2.2. The gravity dependence of the human nervous system
In addition to the abovementioned molecular and cellular experiments, a variety of studies have been conducted to investigate the effect of gravity on the nervous system in humans [4, 10, 30, 31, 32, 33, 34]. In the context of movement control, it becomes apparent that the biophysical attributes underlying cell communication and the nervous capacity to inhibit and facilitate neural pathways are of fundamental importance to activate and control the skeletal muscle, allowing the living organisms to displace themselves. On the complex sensorimotor level, the gravitational force determines human movement control, and its impact is considered to be of major relevance for the astronaut’s safety management in scenarios that require spontaneous or chronic adaptation to an astronomical environment different from the Earth. Not only are short-term platforms as parabolic flights and centrifuges used for this research, the experiments are also conducted during long-term space missions or exploration class missions (up to 1.5 years).
A frequently used technique is the peripheral nerve stimulation (PNS) as it is a noninvasive and reliable approach, providing information about nerve communication including temporal and spatial characteristics of direct motor (M-wave) and reflector responses (Hoffmann(H)-reflex) of the skeletal muscle [35, 36]. By external electrical stimulation, neurons, axons, or cell bodies are depolarized, and the bipolar potential difference of the muscle is measured and interpreted [4]. The nerve tibialis posterior and the muscle soleus have been established as a model for describing the adaptation processes of the neuromuscular system with emphasize on the temporal and spatial characteristics of the electromyographic signal.
2.2.1. Spatial attributes
The shaping of the potential difference includes peak-to-peak amplitudes normalized to the input stimulus and is associated with the magnitude of the muscle output [37]. Furthermore, the stimulation threshold corresponds to the threshold for axonal excitation with a minimal current evoking a muscle contraction [4].
2.2.2. Stimulation threshold of the H-reflex
The needed electrical stimulation to depolarize an axon to generate a constant muscle response can be interpreted as the responsiveness of a nerve to external stimuli. In reduced gravity conditions, similar to Moon (0.16 g) and Mars (0.36 g), generated in parabolic flights, higher stimulation currents for PNS were needed to depolarize the neurons. In hypergravity (1.8 g), the needed currents were smaller [4]. Although the respective partial-gravity level lasts only 24–33 s [10] and effects are reversible within seconds, it can be concluded that the stimulation threshold is acutely increased in reduced gravity and decreased in hypergravity.
2.2.3. Amplitude of the H-reflex
The H-reflex amplitude describes the neuronal output signal of the reflectory reaction of muscles and is proportional to the muscle contraction after peripheral electrical stimulation of sensory fibers in their innervating nerves. Gravity dependency has been reported in cross-sectional study designs with neuroplastic changes for amplitudes of H-reflexes and stretch reflexes [10, 30, 31, 32, 33, 34]. The peak-to-peak amplitudes increased during hypergravity, independently from the method of stimulation [10, 33].
In micro- and reduced gravity, the results are more inhomogeneous. Experiments in Mars and Moon gravity showed a gravity dependence in the decrease of peak-to-peak amplitudes of Hmax. Less gravity resulted in a higher decrease in Hmax amplitude [4]. Nevertheless, in microgravity, the H-reflex was either not changed [10, 34] or it was increased [30, 31, 32, 33]. A long-term experiment on the International Space Station (ISS) revealed a decrease of H-reflexes in space [38]. This decrease was found for 5 months in space, but it was recovered shortly after the return to Earth.
The inhomogeneous findings might be explained by (1) active adaptation processes during long-term missions and (2) mainly due to differences in methodology [4].
The amplitudes of the different sections of the H-reflex depend on the stimulation threshold. As the threshold is gravity-dependent, this has to be taken into account when a constant stimulus intensity is used during the experiments [30, 31, 32, 33]. H/M-wave recruitment curves are independent of stimulation threshold [10, 34]. As a consequence, gravity-induced changes in H-reflex amplitudes elicited with a constant and submaximal stimulus are rather attributed to threshold shifts than changes in gravity [30, 31, 32, 33].
2.2.4. Temporal attributes
Temporal characteristics of motor and reflectory responses are characterized by latencies relying on the nerve’s conduction velocity [39], duration, and inter-peak intervals (IPI) associated with the conduction speed along the muscle fibers at the neuromuscular junction where the nerve interconnects with the muscle [40].
2.2.5. Neuromuscular latency
Neuromuscular latency describes the time between a given stimulus and the measured muscle response. The latency of H-reflex and M-wave in the Soleus muscle was investigated in many experiments, short term [4, 32] and long term [40], but the results are again ambiguous, similar to the findings for the amplitudes of H-reflex. In eight subjects, Ritzmann et al. showed an increase in H-reflex latencies with gradually decreasing gravity (from hyper to 1 g to Mars to Lunar gravity) with a simultaneous tendency of an increase of M-wave latencies [4]. However, Ohira et al. showed that hyper- and microgravity had no immediate effect on the H-reflex and M-wave latencies; unfortunately, they did not give information about the sample size [32].
2.2.6. Inter-peak interval
By interpreting the IPI between the negative and the positive maxima of the biphasic amplitude, information about the conduction velocity from the motor end plate to the muscle fibers can be gained. The motor end plates (or neuromuscular junction) are the interface between the nervous system and the muscles. It could be showed that the IPIs of the peak M. soleus M-wave and H-reflex significantly increase with decreasing gravity from hyper- to 1 g to Mars to Moon gravity conditions [4]. This finding can be interpreted that the conduction velocity at the neuromuscular junction is decreasing in reduced gravity and is increasing in hypergravity. This effect occurs immediately and is fully reversible.
2.2.7. Duration
The duration of the H-reflex is established as the interval from the first rise of the electromyographic signal until return to baseline. Ritzmann et al. demonstrated a gradual decrease in H-reflex duration with increasing gravitation from lunar to Martian to earth gravitation to hypergravity [4]. Accordingly, the duration of the M-waves showed a strong tendency to decline with increasing gravitation. As the duration of the motor and reflectory responses cover information about the conduction velocity of signal transmission from the motor end plate to the muscle fibers, results indicate a major impact of gravity on the temporal characteristics of sensorimotor responses.
3. A model for the immediate adaptation of the nervous system to changes in gravity
The following model integrates the results from the various experiments that have been carried out in the past decades from cellular level up to the neuromuscular interface. To avoid long-term adaptation processes, only immediate effects have been taken into account. The model was designed in a bottom-up approach, starting at the very base level of gravity dependence. Therefore, it can be used as a framework for future—more complex data—as long-term adaptation processes and the gravity dependence of for example, the human brain.
3.1. Molecular level
Micro- and hypergravity change the biophysical properties of biological membranes in every cell in the body. This is not due to some biological effect or process, it is a change in thermodynamic properties of biological membranes [20]; therefore, this can be seen as the basic principle of how gravity affects cells as neuronal cells, for example.
On Earth, it is well known that the properties of membrane-integrated proteins as ion channels depend on the physical state of the membrane. Lateral pressure or membrane fluidity is an important component, for example, the open state of alamethicin pores clearly depends on the lateral pressure of the membrane [41], and the pore activity increases with an increased lateral pressure. An increased lateral pressure can be interpreted as decreased membrane fluidity. This was also shown for other ion channels, for example, the closed-state probability of nicotinic acetylcholine receptor channels increases (the open-state probability decreases) toward decreased membrane fluidity [21].
The pore activity of alamethicin and the open-state probability of ion channels is also gravity-dependent [24, 25]. In microgravity, the open-state probability decreases, whereas in hypergravity, it increases.
As membrane fluidity is affected by gravity and due to the fact that ion channels are affected by membrane fluidity, the first part of the model can be described as follows:
In microgravity, the membrane fluidity is increased. This changed membrane fluidity decreases the open-state probability of ion channels. This effect is inversed in hypergravity: membrane fluidity decreases and the open-state probability of ion channels increases (Figure 1).
Figure 1.
The biophysical gravity dependence of cell membranes and incorporated ion channel proteins. Modified from [42].
3.2. Single cells
It was shown that cells slightly depolarize in microgravity—the membrane potential gets more positive—and they hyperpolarize in hypergravity. With a light depolarization of the resting potential, the threshold to trigger action potentials is reached more easily. This effect was demonstrated in spontaneous active leech neurons. The rate of APs increased in microgravity.
With these findings, the model of gravity dependence on the molecular level can be extended to explain the cellular gravity dependence of single (neuronal) cells (Figure 2).
Figure 2.
The gravity dependence of a single neuronal cell. Modified from [42].
3.3. Neuronal system: Sensorimotor system
The influence of different gravity conditions on neuronal tissue is clearly visible. In isolated single axons as well as in living animals and in human test subjects, the propagation velocity of APs is decreased in microgravity and it is increased in hypergravity.
Neuromuscular reflex arcs in humans are influenced by gravity. In microgravity, increased latencies can be measured. An increased latency can be explained with a decreased conduction speed—the APs are slower in microgravity.
In Mars and Moon gravity, a higher stimulus has to be given to get the same Hmax as in 1 g, and the peak-to peak amplitude of the H-reflex is decreased (with heterogeneous data at real microgravity). Unfortunately, as the methods of single-cell electrophysiology and peripheral nerve stimulation are different, their results cannot be compared directly. Nevertheless, a decreased propagation velocity of APs in the axons can also explain the decrease in Hmax in microgravity. Less APs per time arrive at the muscle, which leads to a reduced contraction. Two findings support this explanation: first, the decrease can be compensated with a higher stimulus. Due to the frequency coding of sensory input, a higher stimulus generates more APs per time. With more APs per time arriving at the muscle, the contraction force is increased. Second, the decrease in inter-peak intervals of the H-reflex indicates a decreased signal speed at the neuromuscular junction. In increased gravity, these effects are reversed (Figure 3).
Figure 3.
The gravity dependence of a multicellular network, connected via synapses as the sensorimotor system. Modified from [42].
In microgravity, the rate of action potentials is increased, while at the same time, the propagation speed of APs is decreased. This might look like an inconsistency, but it is not. It can be explained with a mathematical equation. Matsumoto and Tasaki developed a mathematical model to calculate the conduction speed of APs in unmyelinated axons [43]. This equation can also be used to estimate the conduction velocity of APs in myelinated axons
vaxon≈d8∙ρC2∙R∗E1
where vaxon = conduction velocity, C = membrane capacity, d = diameter of the nerve, R* = resistance of the membrane, and ρ = axoplasmic resistance.
By integrating the data from gravity research and Matsumoto and Tasaki’s model, at first view, the inconsistent findings from single-cell electrophysiology and the data from PNS can be brought together quite nicely to a working model on how the sensorimotor system adapts to changes in gravity.
The increased membrane viscosity in microgravity decreases the open-state probability of ion channels, leading to a slightly depolarized membrane potential. With a reduced open-state probability, the resistance of the membrane (R*) is increased. If axoplasmic resistance (ρ), membrane capacity (C), and the diameter of the axon (d) are treated as constant in changed gravity, the increased resistance of the membrane leads to a decreased conduction velocity of APs (vaxon) while simultaneously APs can be triggered more easily.
To sum up, the described effects are a gravity-dependent decrease in neuronal conduction velocity–or, more general, an increase in electrical and chemical time constants—under reduced gravity and vice versa in hypergravity.
4. Conclusion
In the decades since the first manned space mission, many in vitro and in vivo experiments have been conducted to investigate the effect of micro- and hypergravity on neuronal processes. Adaptation processes occur on all levels of organization, from the subcellular level up to the neuromuscular system (and even up to the brain). Unfortunately, till date, the discrete results of these experiments were never brought together to see (1) whether they can be integrated to a working model of neuronal adaptation in varying gravity or (2) to reveal inconsistencies or (3) areas, which have not been investigated yet. This model aims at bringing insight to the short-term adaptation of the neuronal system to varying gravity conditions. Simultaneously—as some points still are based on reasoned assumptions [42]—it has to be seen as a framework, which should be fleshed out more in future experiments to include long-term adaptation processes and the adaptation of the human brain. A more interconnected and interdisciplinary analysis of all the data can serve as a “roadmap,” aiming for giving more structure to ongoing and future research.
Findings are of major functional relevance in the application field of manned space flight as well as countermeasure development. As more and more space agencies and private space companies are planning long-term missions into space, for example, to Mars, the effect of gravity—and its absence—on the human organisms has to be understood overreaching all vital body systems to minimize the risks for space-faring humans [2]. Today, scientific outcomes of life science experiments executed in samples of astronauts and cosmonauts encompass a variety of long-term adaptation in regard to their sensory perception, motor execution, and planning as well as complex body motion. They are interrelated to neural adaptation to varying gravity and have been verified as follows (for review, see [44]): a recalibration of sensory perception, vestibular and proprioceptive dysfunction [7, 11], changes in muscle synergies and coordination, a decline of muscle force as well as deficits in posture control [6], locomotion, and functional mobility [8]. Reduced and delayed reflex responses and a decline in intramuscular and intermuscular function occur concomitantly with an increased muscle weakness, fatigue concomitant with a higher fall, and injury prevalence [40, 44]. With a persistency beyond the acute period of space flight, these adaptations are of clinical relevance as manifested by significant adverse effects which entail fragility and bone fractures [14, 44].
To reduce health and life risk throughout long-term exposure to low gravity during manned space explorations, scientists and space agencies developed intelligent exercise technologies and efficient interventions validated in cohorts of space crew members to prevent the human body from deconditioning [2]. Empirical outcomes subject to the NS and its adaptability to changes in gravity are included in the concepts of ancient and future countermeasures as manifested, for instance, for strength or jump exercises, vibration treatment, sensorimotor training, and artificial gravity [44].
Although great efforts have been made to optimize countermeasures, limitation on the cellular level such as changes in membrane fluidity as well as complex adaptations on the spinal level encompassing mechanisms of facilitating and inhibiting is of major relevance and cannot be diminished by countermeasures, only [4, 10, 23].
As astronauts traveling to Mars will live in the absence of gravity for approximately 2 years with transition between weightlessness and planetary gravitational forces at the beginning, middle, and end of the mission, further research and countermeasure development considering the gravity dependency of the NS will be obligate to assure a safe space travel and Earth return in the future [44].
Acknowledgments
The research was supported by the German Aerospace Center (DLR), the European Space Agency (ESA), and Novespace.
\n',keywords:"gravity, microgravity, hypergravity, adaptation, reflex, sensorimotor function, biophysical properties, electrophysiology",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/59699.pdf",chapterXML:"https://mts.intechopen.com/source/xml/59699.xml",downloadPdfUrl:"/chapter/pdf-download/59699",previewPdfUrl:"/chapter/pdf-preview/59699",totalDownloads:863,totalViews:1057,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:1,dateSubmitted:"December 1st 2017",dateReviewed:"January 31st 2018",datePrePublished:null,datePublished:"May 30th 2018",dateFinished:null,readingETA:"0",abstract:"Gravity affects the nervous system of living organisms. This book chapter reviews historical and recent findings on how changes in gravity affect cellular and subcellular parameters of human and animal cells as well as the timing and shaping of complex sensorimotor responses. With an emphasize on weightlessness, partial, and hypergravity conditions, the gravity dependencies of living organisms have been manifested on different levels of organization, ranging from changes in biophysical properties of single cells to the intact nervous system. An effort has been made to integrate the various findings into a consistent model for a better understanding of how the components of the nervous system interact as a response to acute and long-term gravitational variation. Especially with planned long-term manned missions to Mars and beyond, knowledge about the impact of increased and decreased gravity on the nervous system is essential for the physical and cognitive preparation to assure the success of space missions and human survival in space.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/59699",risUrl:"/chapter/ris/59699",book:{slug:"into-space-a-journey-of-how-humans-adapt-and-live-in-microgravity"},signatures:"Florian P.M. Kohn, Claudia Koch and Ramona Ritzmann",authors:[{id:"148496",title:"Dr.",name:"Florian",middleName:"Peter Michael",surname:"Kohn",fullName:"Florian Kohn",slug:"florian-kohn",email:"Florian.P.M.Kohn@uni-hohenheim.de",position:null,institution:{name:"University of Hohenheim",institutionURL:null,country:{name:"Germany"}}},{id:"238072",title:"Dr.",name:"Claudia",middleName:null,surname:"Koch",fullName:"Claudia Koch",slug:"claudia-koch",email:"claudia.ulbrich@uni-hohenheim.de",position:null,institution:null},{id:"238073",title:"Dr.",name:"Ramona",middleName:null,surname:"Ritzmann",fullName:"Ramona Ritzmann",slug:"ramona-ritzmann",email:"ramona.ritzmann@sport.uni-freiburg.de",position:null,institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Gravity and the nervous system",level:"1"},{id:"sec_2_2",title:"2.1. The gravity dependence of subcellular and cellular parameters",level:"2"},{id:"sec_3_2",title:"2.1.1. Membrane parameters",level:"2"},{id:"sec_4_2",title:"2.1.2. Ion channel parameters",level:"2"},{id:"sec_5_2",title:"2.1.3. Electrophysiological properties of single cells",level:"2"},{id:"sec_6_2",title:"2.1.4. Propagation of action potentials",level:"2"},{id:"sec_8",title:"2.2. The gravity dependence of the human nervous system",level:"1"},{id:"sec_8_2",title:"2.2.1. Spatial attributes",level:"2"},{id:"sec_9_2",title:"2.2.2. Stimulation threshold of the H-reflex",level:"2"},{id:"sec_10_2",title:"2.2.3. Amplitude of the H-reflex",level:"2"},{id:"sec_11_2",title:"2.2.4. Temporal attributes",level:"2"},{id:"sec_12_2",title:"2.2.5. Neuromuscular latency",level:"2"},{id:"sec_13_2",title:"2.2.6. Inter-peak interval",level:"2"},{id:"sec_14_2",title:"2.2.7. Duration",level:"2"},{id:"sec_16",title:"3. A model for the immediate adaptation of the nervous system to changes in gravity",level:"1"},{id:"sec_16_2",title:"3.1. Molecular level",level:"2"},{id:"sec_17_2",title:"3.2. Single cells",level:"2"},{id:"sec_18_2",title:"3.3. Neuronal system: Sensorimotor system",level:"2"},{id:"sec_20",title:"4. Conclusion",level:"1"},{id:"sec_21",title:"Acknowledgments",level:"1"}],chapterReferences:[{id:"B1",body:'Montmerle T, Augereau JC, Chaussidon M, Gounelle M, Marty B, Morbidelli A. Solar system formation and early evolution: The first 100 million years. Earth, Moon and Planets. 2006;98(1):39-95'},{id:"B2",body:'Spudis PD. An argument for human exploration of the moon and Mars. American Scientist. 1992;80:269-277'},{id:"B3",body:'Lister A, Rothschild LJ, editors. Evolution on Planet Earth: The Impact of the Physical Environment. Amsterdam, Boston: Academic Press; 2003'},{id:"B4",body:'Ritzmann R, Krause A, Freyler K, Gollhofer A. Gravity and neuronal adaptation - Neurophysiology of reflexes from hypo to hyper gravity conditions. Microgravity Science and Technology. 2017;29(1-2):9-18. DOI: 10.1007/s12217-016-9519-4'},{id:"B5",body:'Margaria R, Cavagna GA. 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Conduction velocity of nerve and muscle fiber action potentials after a space mission or a bed rest. Clinical Neurophysiology. 2003;114:86-93'},{id:"B41",body:'Hanke W, Schluhe W. R. Planar Lipid Bilayer Experiments: Techniques and Application. Oxford: Academic Press; 1993'},{id:"B42",body:'Kohn FPM, Ritzmann R. Gravity and neuronal adaptation, in vitro and in vivo-from neuronal cells up to neuromuscular responses: A first model. European Biophysics Journal: EBJ. DOI: 10.1007/s00249-017-1233-7'},{id:"B43",body:'Matsumoto G, Tasaki I. A study of conduction velocity in nonmyelinated nerve fibers. Biophysical Journal. 1977;20:1-13'},{id:"B44",body:'Hilbig R, Gollhofer A, Bock O, Manzey D. Sensory Motor and Behavioral Research in Space. Cham: Springer International Publishing; 2017'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Florian P.M. Kohn",address:"florian.p.m.kohn@uni-hohenheim.de",affiliation:'
Department of Membrane Physiology (230b), University of Hohenheim, Germany
Departments for Sports and Sport Science, University of Freiburg, Germany
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Woods",slug:"benjamin-k.s.-woods"}]},{id:"28803",title:"A Probabilistic Approach to Fatigue Design of Aerospace Components by Using the Risk Assessment Evaluation",slug:"a-probabilistic-approach-to-fatigue-design-of-aerospace-components-by-using-the-risk-assessment-eval",signatures:"Giorgio Cavallini and Roberta Lazzeri",authors:[{id:"114308",title:"Prof.",name:"Giorgio",middleName:null,surname:"Cavallini",fullName:"Giorgio Cavallini",slug:"giorgio-cavallini"},{id:"119256",title:"Dr.",name:"Roberta",middleName:null,surname:"Lazzeri",fullName:"Roberta Lazzeri",slug:"roberta-lazzeri"}]},{id:"28804",title:"Study of Advanced Materials for Aircraft Jet Engines Using Quantitative Metallography",slug:"study-of-advanced-materials-for-aircraft-jet-engines-using-quantitative-metallography-",signatures:"Juraj Belan",authors:[{id:"111991",title:"Dr.",name:"Juraj",middleName:null,surname:"Belan",fullName:"Juraj Belan",slug:"juraj-belan"}]},{id:"28805",title:"ALLVAC 718 Plus™ Superalloy for Aircraft Engine Applications",slug:"allvac-718-plus-superalloy-for-aircraft-engine-applications",signatures:"Melih Cemal Kushan, Sinem Cevik Uzgur, Yagiz Uzunonat and Fehmi Diltemiz",authors:[{id:"109083",title:"Dr.",name:"Melih Cemal",middleName:null,surname:"Kushan",fullName:"Melih Cemal Kushan",slug:"melih-cemal-kushan"},{id:"117212",title:"MSc.",name:"Sinem",middleName:null,surname:"Cevik",fullName:"Sinem Cevik",slug:"sinem-cevik"},{id:"117213",title:"MSc.",name:"Yagız",middleName:null,surname:"Uzunonat",fullName:"Yagız Uzunonat",slug:"yagiz-uzunonat"},{id:"117214",title:"Dr.",name:"Fehmi",middleName:null,surname:"Diltemiz",fullName:"Fehmi Diltemiz",slug:"fehmi-diltemiz"}]},{id:"28806",title:"Potential of MoSi2 and MoSi2-Si3N4 Composites for Aircraft Gas Turbine Engines",slug:"potential-of-mosi2-and-mosi2-si3n4-composites-for-aircraft-gas-turbine-engines",signatures:"Melih Cemal Kushan, Yagiz Uzunonat, Sinem Cevik Uzgur and Fehmi Diltemiz",authors:[{id:"109083",title:"Dr.",name:"Melih Cemal",middleName:null,surname:"Kushan",fullName:"Melih Cemal Kushan",slug:"melih-cemal-kushan"},{id:"117212",title:"MSc.",name:"Sinem",middleName:null,surname:"Cevik",fullName:"Sinem Cevik",slug:"sinem-cevik"},{id:"117213",title:"MSc.",name:"Yagız",middleName:null,surname:"Uzunonat",fullName:"Yagız Uzunonat",slug:"yagiz-uzunonat"},{id:"117471",title:"Dr.",name:"Fehmi",middleName:null,surname:"Diltemiz",fullName:"Fehmi Diltemiz",slug:"fehmi-diltemiz"}]},{id:"28807",title:"An Algorithm for Parameters Identification of an Aircraft’s Dynamics",slug:"an-algorithm-for-parameters-identification-of-an-aircraft-s-dynamics-",signatures:"I. A. Boguslavsky",authors:[{id:"109029",title:"Prof.",name:"Josif",middleName:null,surname:"Boguslavskiy",fullName:"Josif Boguslavskiy",slug:"josif-boguslavskiy"}]},{id:"28808",title:"Influence of Forward and Descent Flight on Quadrotor Dynamics",slug:"influence-of-forward-and-descent-flight-on-quadrotor-dynamics",signatures:"Matko Orsag and Stjepan Bogdan",authors:[{id:"21850",title:"Dr.",name:"Stjepan",middleName:null,surname:"Bogdan",fullName:"Stjepan Bogdan",slug:"stjepan-bogdan"},{id:"112769",title:"Mr.",name:"Matko",middleName:null,surname:"Orsag",fullName:"Matko Orsag",slug:"matko-orsag"}]},{id:"28809",title:"Advanced Graph Search Algorithms for Path Planning of Flight Vehicles",slug:"advanced-graph-search-algorithms-for-path-planning-of-flight-vehicles",signatures:"Luca De Filippis and Giorgio Guglieri",authors:[{id:"110942",title:"Dr.",name:"Luca",middleName:null,surname:"De Filippis",fullName:"Luca De Filippis",slug:"luca-de-filippis"},{id:"117083",title:"Prof.",name:"Giorgio",middleName:null,surname:"Guglieri",fullName:"Giorgio Guglieri",slug:"giorgio-guglieri"}]},{id:"28810",title:"GNSS Carrier Phase-Based Attitude Determination",slug:"gnss-carrier-phase-based-attitude-determination",signatures:"Gabriele Giorgi and Peter J. G. Teunissen",authors:[{id:"116970",title:"Prof.",name:"Peter",middleName:null,surname:"Teunissen",fullName:"Peter Teunissen",slug:"peter-teunissen"},{id:"116988",title:"MSc.",name:"Gabriele",middleName:null,surname:"Giorgi",fullName:"Gabriele Giorgi",slug:"gabriele-giorgi"}]},{id:"28811",title:"A Variational Approach to the Fuel Optimal Control Problem for UAV Formations",slug:"a-variational-approach-to-the-fuel-optimal-control-problem-for-uav-formations",signatures:"Andrea L’Afflitto andWassim M. Haddad",authors:[{id:"109152",title:"Mr.",name:"Andrea",middleName:null,surname:"LAfflitto",fullName:"Andrea LAfflitto",slug:"andrea-lafflitto"},{id:"137973",title:"Prof.",name:"Wassim M.",middleName:null,surname:"Haddad",fullName:"Wassim M. Haddad",slug:"wassim-m.-haddad"}]},{id:"28812",title:"Measuring and Managing Uncertainty Through Data Fusion for Application to Aircraft Identification System",slug:"measuring-and-managing-uncertainty-through-data-fusion-on-target-identification",signatures:"Peter Pong and Subhash Challa",authors:[{id:"109333",title:"Mr.",name:"Peter",middleName:null,surname:"Pong",fullName:"Peter Pong",slug:"peter-pong"},{id:"142023",title:"Prof.",name:"Subhash",middleName:null,surname:"Challa",fullName:"Subhash Challa",slug:"subhash-challa"}]},{id:"28813",title:"Subjective Factors in Flight Safety",slug:"subjective-factors-in-flight-safety",signatures:"Jozsef Rohacs",authors:[{id:"114528",title:"Prof.",name:"Jozsef",middleName:null,surname:"Rohacs",fullName:"Jozsef Rohacs",slug:"jozsef-rohacs"}]},{id:"28814",title:"Power Generation and Distribution System for a More Electric Aircraft - A Review",slug:"more-electric-aircraft",signatures:"Ahmed Abdel-Hafez",authors:[{id:"112126",title:"Dr.",name:"Ahmed",middleName:"AbdElmalek",surname:"Abdel-Hafez",fullName:"Ahmed Abdel-Hafez",slug:"ahmed-abdel-hafez"}]},{id:"28815",title:"Power Electronics Application for More Electric Aircraft",slug:"power-electronics-application-for-more-electric-aircraft",signatures:"Mohamad Hussien Taha",authors:[{id:"111999",title:"Dr.",name:"Mohamad",middleName:null,surname:"Taha",fullName:"Mohamad Taha",slug:"mohamad-taha"}]},{id:"28816",title:"Key Factors in Designing In-Flight Entertainment Systems",slug:"key-factors-in-designing-in-flight-entertainment-systems",signatures:"Ahmed Akl, Thierry Gayraud and Pascal Berthou",authors:[{id:"10153",title:"Prof.",name:"Thierry",middleName:"Henri",surname:"Gayraud",fullName:"Thierry Gayraud",slug:"thierry-gayraud"},{id:"10766",title:"Dr.",name:"Pascal",middleName:null,surname:"Berthou",fullName:"Pascal Berthou",slug:"pascal-berthou"},{id:"111784",title:"Prof.",name:"Ahmed",middleName:null,surname:"Akl",fullName:"Ahmed Akl",slug:"ahmed-akl"}]},{id:"28817",title:"Methods for Analyzing the Reliability of Electrical Systems Used Inside Aircrafts",slug:"analyze-methods-of-reliability-of-electrical-systems-",signatures:"Nicolae Jula and Cepisca Costin",authors:[{id:"115164",title:"Prof.",name:"Nicolae",middleName:null,surname:"Jula",fullName:"Nicolae Jula",slug:"nicolae-jula"},{id:"116674",title:"Prof.",name:"Costin",middleName:null,surname:"Cepisca",fullName:"Costin Cepisca",slug:"costin-cepisca"}]},{id:"28818",title:"Automatic Inspection of Aircraft Components Using Thermographic and Ultrasonic Techniques",slug:"automatic-inspection-of-aircraft-components-using-thermographic-and-ultrasonic-techniques",signatures:"Marco Leo",authors:[{id:"109814",title:"Dr.",name:"Marco",middleName:null,surname:"Leo",fullName:"Marco Leo",slug:"marco-leo"}]},{id:"28819",title:"The Analysis of the Maintenance Process of the Military Aircraft",slug:"the-analysis-of-the-maintenance-process-of-the-military-aircraft-",signatures:"Mariusz Wazny",authors:[{id:"115049",title:"Dr.",name:"Mariusz",middleName:null,surname:"Wazny",fullName:"Mariusz Wazny",slug:"mariusz-wazny"}]},{id:"28820",title:"Review of Technologies to Achieve Sustainable (Green) Aviation",slug:"review-of-technologies-to-achieve-sustainable-green-aviation",signatures:"Ramesh K. Agarwal",authors:[{id:"38519",title:"Prof.",name:"Ramesh K.",middleName:null,surname:"Agarwal",fullName:"Ramesh K. Agarwal",slug:"ramesh-k.-agarwal"}]},{id:"28821",title:"Synthetic Aperture Radar Systems for Small Aircrafts: Data Processing Approaches",slug:"synthetic-aperture-radar-systems-for-small-aircrafts-data-processing-approaches",signatures:"Oleksandr O. Bezvesilniy and Dmytro M. Vavriv",authors:[{id:"117110",title:"Dr.",name:"Oleksandr",middleName:null,surname:"Bezvesilniy",fullName:"Oleksandr Bezvesilniy",slug:"oleksandr-bezvesilniy"},{id:"117117",title:"Prof.",name:"Dmytro",middleName:null,surname:"Vavriv",fullName:"Dmytro Vavriv",slug:"dmytro-vavriv"}]},{id:"28822",title:"Avionics Design for a Sub-Scale Fault- Tolerant Flight Control Test-Bed",slug:"avionic-design-for-a-sub-scale-fault-tolerant-flight-control-test-bed-",signatures:"Yu Gu, Jason Gross, Francis Barchesky, Haiyang Chao and Marcello Napolitano",authors:[{id:"116275",title:"Dr.",name:"Yu",middleName:null,surname:"Gu",fullName:"Yu Gu",slug:"yu-gu"},{id:"117375",title:"Mr.",name:"Francis",middleName:null,surname:"Barchesky",fullName:"Francis Barchesky",slug:"francis-barchesky"},{id:"117376",title:"Dr.",name:"Haiyang",middleName:null,surname:"Chao",fullName:"Haiyang Chao",slug:"haiyang-chao"},{id:"117377",title:"Dr.",name:"Marcello",middleName:null,surname:"Napolitano",fullName:"Marcello Napolitano",slug:"marcello-napolitano"},{id:"117379",title:"Mr.",name:"Jason",middleName:null,surname:"Gross",fullName:"Jason Gross",slug:"jason-gross"}]},{id:"28823",title:"Study of Effects of Lightning Strikes to an Aircraft",slug:"study-of-effects-of-lightning-strikes-to-an-aircraft",signatures:"N.I. Petrov, A. Haddad, G.N. Petrova, H. Griffiths and R.T. Waters",authors:[{id:"109170",title:"Dr.",name:"Nikolai",middleName:"I",surname:"Petrov",fullName:"Nikolai Petrov",slug:"nikolai-petrov"},{id:"116925",title:"Prof.",name:"A",middleName:null,surname:"Haddad",fullName:"A Haddad",slug:"a-haddad"},{id:"116927",title:"Dr.",name:"H",middleName:null,surname:"Griffiths",fullName:"H Griffiths",slug:"h-griffiths"},{id:"116928",title:"Mrs.",name:"Galina",middleName:null,surname:"Petrova",fullName:"Galina Petrova",slug:"galina-petrova"},{id:"116929",title:"Prof.",name:"R.T.",middleName:null,surname:"Waters",fullName:"R.T. Waters",slug:"r.t.-waters"}]}]}]},onlineFirst:{chapter:{type:"chapter",id:"66118",title:"Combating Alarm Fatigue: The Quest for More Accurate and Safer Clinical Monitoring Equipment",doi:"10.5772/intechopen.84783",slug:"combating-alarm-fatigue-the-quest-for-more-accurate-and-safer-clinical-monitoring-equipment",body:'\n
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1. Introduction
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Highly reliable, precise, user-friendly, and cost-effective clinical alarm systems are critical to efficient functioning of health-care facilities [1, 2, 3]. Despite tremendous progress over the past few decades, the “perfect solution” remains elusive, with focus being placed primarily on clinical indications and appropriateness of use for the existing equipment and monitoring frameworks [3, 4, 5, 6]. Beyond the concept of “false alarm,” suboptimal implementation of clinical monitoring systems can have much more profound and potentially dangerous consequences [7, 8, 9]. One such consequence, and the primary topic of this chapter, is the phenomenon of alarm fatigue (AF). It is defined as the decrease of clinician response caused by excessive alarms, sensory overload, and desensitization, in addition to other occupational and environmental variables [9, 10, 11]. Among contributing factors are also high staff workload, long shift hours, and work environments with high noise levels, all of which contribute to the “desensitization effect” associated with AF [10, 12].
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Hospital patient care units tend to be high-paced and potentially unpredictable environments, with complex workflows. Multiple simultaneous interactions between patients, families, and health-care staff may create an added element of chaos [13, 14]. To help nurses and other staff cope with their many responsibilities, various audible and visual alerts have been implemented to prompt immediate response and clinical assessment of patients [15]. These alerts are relayed from patient monitoring devices, which provide continuous flow of vital sign data with a high degree of sensitivity. The advanced technology used in these surveillance systems has provided a significant amount of physiological data at low cost while being particularly helpful by facilitating the monitoring of critically ill patients to identify deviations of vital signs (e.g., heart rate, respiratory rate, blood pressure, and pulse oximetry) from normal ranges [16]. However, when various clinical alarm systems are superimposed on the need for constant vigilance in the setting of highly challenging and often chaotic environment of the typical clinical unit, the stage is set for the emergence of AF and other forms of cognitive lapses [17, 18, 19].
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The prevalence of various monitoring modalities has increased significantly, with most health-care institutions utilizing some broadly defined combination of different alarm systems. As the use of these systems became more widespread, a major flaw became evident: the excessive amount of triggered alarms was contributing to unintended consequences, both in terms of patient outcomes and staff fatigue/dissatisfaction [8, 20, 21]. The high rate of nonactionable alarms, where immediate action is not required on the behalf of clinicians, was especially problematic [22]. In fact, the increasing frequency of “false alarms” has a significant desensitization effect on hospital staff, whereby some alarms may be erroneously “dismissed by assumption” as being “noncritical” [23]. This desensitization leads to both increased response times and decreased, or even lack of, clinician response. In the setting of a busy hospital, it is commonplace to hear constant chimes and beeps, each coming from different machines and indicating different “alarm conditions” (Figure 1). It should be more of an expectation that clinicians become desensitized to extraneous stimuli given the constant sensory bombardment coupled with the need for vigilance and differential interpretation of each alarm [25, 26]. When further compounded by heavy clinical workloads and long shifts, it becomes a matter of “statistical probability” before a critical alarm is missed [27, 28, 29]. Given the effect of this potentially dangerous phenomenon on both quality and safety of patient care, closer scrutiny of AF and related concepts is warranted. In this chapter, we will present a vignette-based discussion outlining fairly typical AF scenarios. Opportunities for improvement, including equipment, personnel, and systems-based considerations, will then be provided.
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Figure 1.
Conceptual model for daily observed alarms at a typical acute care hospital. Data shown in proportion to different scales, from individual patient to entire institution, showing the true magnitude of the problem (source: Ref. [24]).
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2. Primary research methods
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For the purposes of this chapter, the authors performed a thorough literature search using PubMed, Google Scholar™, and Bioline International. Primary search terms included “alarm fatigue,” “health-care alarms,” “patient monitoring,” “provider burnout,” as well as secondary terms consisting of various combinations of primary search terms. From over 47,000 unique search results, we distilled 73 most pertinent references immediately relevant to this document. Finally, additional sources that were cited across our primary search results were added, for a total of 101 references included in the final manuscript.
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3. Patient monitoring: different types and modalities
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A diverse number of patient monitors are widely used across various health-care settings [30, 31, 32]. When employed correctly, they provide potentially valuable, actionable, and real-time information about a patient’s clinical status. Different monitoring devices are intended to measure different parameters, potentially allowing for rapid assessment of a patient. This is especially relevant in the context of the current discussion of AF and more specifically the domain of alarm trigger accuracy [32, 33]. As clinical monitoring becomes more sophisticated and better integrated, remote (off-site) implementations also become possible [34, 35, 36]. The subsequent discussion will outline major types of monitoring equipment and alarms, including ventilation/oxygenation, hemodynamic, and pressure point alert systems.
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3.1. Ventilation/oxygenation alarms
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In general, primary ventilation/oxygenation alarms (VOA) include capnography and pulse oximetry, respectively. More broadly, respiratory parameter monitoring indicates the patient’s oxygen saturation, respiratory rate, and end-tidal carbon dioxide [33, 37]. The use of VOA has been particularly important for critically ill patients who require mechanical ventilatory support. In such applications, the monitor is designed to be exquisitely sensitive to detect even the slightest changes in a patient’s oxygenation or ventilation status [38]. As demonstrated in Clinical Vignette #1 later in the chapter, an alarm may be triggered following the detection of a very small respiratory parameter “excursion,” regardless of its clinical significance or magnitude of the observed change in the patient’s actual clinical status. In this context, apnea and minute volume warnings are among the most common alarms triggered, with majority of such occurrences deemed clinically irrelevant upon further interrogation [39]. Moreover, many VOA triggers can be attributed to artifactual sources (e.g., patient movement, interruption of blood flow by inflating blood pressure cuff, and even atmospheric pressure variations) [37]. Thus, providers should be educated accordingly to ensure that the above considerations are appropriately factored into final clinical determinations and decisions.
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3.2. Hemodynamic alarms
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Hemodynamic alarms (HA) monitor a variety of parameters, of which the most common ones include heart rate, systolic/diastolic/mean blood pressure, and various other intravascular pressure measurements via both invasive and noninvasive approaches [37, 40]. Hemodynamic monitoring has become a useful tool for the bedside assessment of patients in a number of clinical scenarios, from routine telemetry applications to advanced intravascular catheter utilization. There is some degree of predictability based on measured parameters, especially when trend determination and volume responsiveness are being considered [41, 42]. Hemodynamic monitors are particularly important in the setting of an unstable (or potentially unstable) patient, similar to the one described in Clinical Vignette #3 later in the chapter. In such capacity, HAs can help facilitate rapid intervention and prompt correction of emergent issues. Still, HAs are far from perfect, with significant shortcomings in their discriminatory capabilities. More specifically, HAs are unable to identify a patient as “stable” or “unstable,” especially when physiologic compensatory processes mask any underlying instability or in the setting of rapid change in hemodynamic status [43]. Thus, when using any particular monitoring modality, there is no substitute for an astute clinician who is able to effectively correlate HA findings with the clinical reality [44, 45, 46].
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3.3. Bed and chair pressure sensors
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Bed and chair pressure sensor (BCPS) alarms are utilized across many hospitals and other health-care facilities to help reduce mechanical falls among patients who experience ambulatory or balance difficulties [47, 48]. Falls typically occur as patients attempt to mobilize and/or ambulate without the required assistance of trained health-care staff [49]. Consequently, the use of BCPS alarms serves to alert staff—typically by a pressure-sensitive mechanism—when a patient attempts to move from a bed or chair without assistance. However, the weight-sensitive pads are easily triggered by very slight patient movement, resulting in a significant number of false alarms [50, 51]. This challenge was readily apparent in Clinical Vignette #3 later in the chapter, as the majority of BCPS alerts were likely due to the patient merely shifting slightly in the bed, and not by an actual attempt to independently mobilize and/or ambulate. Unfortunately, the one true positive alarm became lost in “a sea of false negatives.” The practicality of BCPS alarms is also diminished by the inability of staff members to immediately assess/respond to the triggered alarm. Instances have been noted in which the alarm signal is transmitted after the event already transpired, as patients tend to fall immediately upon leaving the bed or chair [52].
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In summary, the above-referenced monitor/alarm types have become an important part of the modern health-care fabric. Despite their ubiquitous use and great potential for constructive and practical clinical application, each type of device carries inherent flaws that providers must be aware of. Detailed knowledge of the risk-benefit equation associated with each device and clinical alarm type is important not only for patient safety but also required to help improve the quality and accuracy of the next generation of monitoring devices.
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4. Patient monitor alarm design
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Patient monitors are designed to have high sensitivity to predefined changes in various measured parameters, including vital signs, respiratory/ventilator status, and patient movements. However, the major drawback associated with high alarm sensitivity is the poor specificity and inherently disproportionate number of nonactionable (or nonclinical) alarms triggered [22, 53, 54]. Depending on the specific alarm and clinical setting, the estimated in range of “false positives” may be as high as 80–99% of all triggered alarms [8]. Broadly speaking, nonactionable alarms can be categorized as false alarms, nuisance alarms, and technical alarms (Figure 1). To elaborate further, false alarms occur in the absence of an actual patient or system trigger and typically result from a measurement artifact [55]. Technical alarms mandate the provider to attend to some operational aspect of the monitoring system, such as when readjustment of monitor leads/sensors is required [21]. Nuisance alarms are defined as clinically insignificant alarms that may interfere with patient care [10]. In aggregate, these nonactionable alarms are a major cause of the overall desensitization of hospital staff that may ultimately result in AF (Figure 2).
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Figure 2.
Schematic representation of the classification of alarm types triggered by various patient monitoring systems, including both actionable and nonactionable alerts (source: Ruskin [8]; Gorges [66]; and Tsien [67]).
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Furthermore, to be effective, the alarms transmitted by monitoring systems must trigger some degree of cognitive response in health-care providers. This equates to introducing stress and the need for constant vigilance, both of which further heighten the risk of AF [56, 57]. When multiple clinical competing priorities collide, it becomes increasingly difficult for a provider to proactively address all ongoing problems, thus forcing them to resort to only partially addressing acute issues while at the same time disrupting other (parallel) activities due to multitasking [58, 59, 60, 61]. Consequently, an ideal alarm should be perfectly audible and easily recognized by health-care providers working within the patient care unit [8], while at the same time minimizing the amount of stress imposed on the responding clinical staff.
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The increasingly complex environment of modern health-care systems has led to several important considerations regarding the practical application of monitoring systems. For example, space-related issues deserve special mention, with overly crowded clinical units creating an abundance of alarm-related stimuli and geographically larger clinical units presenting a barrier to prompt patient access. Elevated acuity and high patient throughput are also important considerations in this context [62].
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Furthermore, technological advancements facilitated the development of increasingly sophisticated alarm systems, with novel features designed to decrease the nuisance factor of the alert mechanism while preserving the level of overall clinical vigilance [63, 64]. These are intended to provide a range of alarm tones that allow care providers to easily identify and prioritize alarms, typically as high, medium, or low priority. However, the implementation of such systems (e.g., IEC 60601-1-8 standard) has presented challenges in terms of recognizability of melodic alarm tones. More specifically, nurses found it difficult to accurately identify all of the melodic tones signifying high-priority alarms, in addition to the potential for confusion between certain alarm pairs [65]. An example of such phenomenon is presented in Clinical Vignette #1 below, where two sets of tones were too difficult for the nurse to readily differentiate, rendering the alarm feature ineffective. Consequently, it is important for systems to have some degree of built-in learnability and flexible discriminative ability, with continued refinement, development, and testing of each clinical alarm, both alone and in tandem with other competing alarms [65]. Without exception, any observed deficits in patient monitor effectiveness and/or safety should prompt an immediate critical evaluation of both technical and clinical aspects of its implementation and function.
A 62-year-old female was admitted to the local hospital 5 days ago due to chronic obstructive pulmonary disease (COPD) exacerbation. She was diagnosed with COPD several years prior and remained stable with no history of exacerbations until 1 week ago when she developed a progressively worsening cough. Soon after her symptoms worsened, she began to feel shortness of breath that was not relieved by rest. At this point, her family insisted she go to the hospital for evaluation. Upon arriving in the emergency department, short-acting bronchodilators and oral corticosteroids were administered with only mild symptomatic improvement. Given the patient’s dyspnea at rest, as well as decreased oxygen saturation of 86%, she was admitted to the pulmonology unit. Supplemental oxygen and intravenous corticosteroids were administered.
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At admission, continuous pulse oximetry monitoring was started. The patient’s hypoxemia seemed to improve slightly over the next 4 days, with oxygen saturation climbing to 88–90% range. Still, the patient’s ventilatory monitor sent alarm signals to the hospital staff several times an hour due to high respiratory rate and episodic oxygen desaturations. Alarm signals were transmitted as either a single low tone (respiratory rate) or a double alarm (desaturations), alternating between low and medium tones. The difference of alarm tone indicated the range in which the patient’s oxygen saturation was measured, but the assigned night-shift nurse found the tones to be too difficult to distinguish and would routinely just perform an in-person check of the saturation level upon entering the room. Throughout the first two nights, the same nurse responded to the alarms in a timely fashion, only to find the patient stable and with no signs of acute distress. Assuming that alarms are unlikely to represent any actionable clinical events, the same nurse then began to silence the sounds and began checking on the patient hourly. In the early morning hours of the fourth day, the nurse silenced the alarm once again, intending to assess the patient once the remainder of her rounding routine was completed. When the nurse finally came to the patient’s room an hour later, she found the patient unresponsive and cyanotic. A rapid assessment showed an oxygen saturation of 79%. The patient was immediately intubated, transferred to intensive care unit, and mechanical ventilation was initiated.
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5.2. Clinical Vignette #2: 65-year-old male transferred to inpatient unit following a total knee arthroplasty
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A 65-year-old male with a history of osteoarthritis of the right knee and refractory pain underwent preoperative evaluation by an orthopedic surgeon. Given his adequate performance status and lack of comorbidities, the patient was determined to be a suitable candidate for total right knee arthroplasty. The surgical procedure was uneventful, with appropriate antibiotic and venous thrombosis prophylaxis administered perioperatively. Following a brief recovery in the postanesthesia care unit, the patient was transferred to the inpatient floor with expected discharge within 5 days postsurgery. Due to the nature of his surgery and apparent fall risk, the patient’s room was fitted with weight-sensitive bed and chair alarms. During the first 3 days, he remained relatively sedated due to the frequent administration of pain medications. However, as the patient began to regain strength, his analgesia regimen was tapered. On day 4, the concurrent increase in patient’s movement began to trigger his bed monitor to the point where the on-call nurse was receiving nearly constant alarm notifications. Multiple times, the nurse entered to assess the patient only to find him resting comfortably without apparent attempt to leave his bed. Later that night, after leaving the patient’s room, the nurse was unexpectedly assigned to three additional patients due to an unplanned absence of a coworker. As the nurse hurried to assess the new patients, the bed monitor transmitted yet another alarm signal. Annoyed by the repeated negative alarms, the nurse disabled the alerts from the bed monitor, intending to check in after tending to her newly assigned patients. When she finally returned to the patient’s room, she found him sprawled on the floor and writhing in pain. The patient, emboldened by his rapid recovery, had attempted to ambulate to the bathroom without assistance and lost his balance in the process. The intense pain prevented him from reaching the call button on the hospital bed, so he was forced to lie on the floor in pain for approximately 1 h. A subsequent skeletal survey revealed a left hip fracture, which required additional surgery, prolonged hospital stay, and the need for inpatient rehabilitation stay due to temporary disability involving bilateral lower extremities (e.g., right knee arthroplasty and left hip injury).
\n
\n
\n
5.3. Clinical Vignette #3: 71-year-old male with history of multiple myeloma admitted for right lower extremity swelling associated with minor pain
\n
A 71-year-old male with a history of multiple myeloma was admitted to the urgent care center after noticing sudden onset of right lower extremity swelling associated with minor pain. The patient began induction therapy for multiple myeloma approximately 1 year prior, achieving adequate disease control. He was subsequently transitioned to maintenance treatment, which he continued for the past 6 months. Evaluation in the urgent care center with venous duplex studies revealed a deep venous thrombosis (DVT). Because of the patient’s established history of malignancy, the triage clinician opted for hospital admission and therapeutic anticoagulation. While being transferred to the inpatient unit, unfractionated heparin anticoagulation was started. Per standard protocol, monitoring equipment was hastily fitted to the patient for noninvasive measurement of his blood pressure and heart rate. Overnight, the patient remained stable, with some resolution of lower extremity of pain despite persistent swelling. The on-call physician assessed the patient during morning rounds and ordered to repeat venous duplex for the afternoon to evaluate for resolution/progression of the DVT. Of note, throughout the night and into the morning hours, the patient’s hemodynamic monitor had been sending intermittent alarm signals. With the first few alarms, the charge nurse promptly responded and quickly assessed the patient for any signs of instability or distress. However, as the shift progressed, the nurse increasingly dismissed repeated signals as “false alarms” due to a recurring pattern of mildly elevated blood pressure and heart rate secondary to episodic extremity pain. Because the inpatient unit continued to be understaffed during the morning shift, the charge nurse decided to disable the patient’s repeated monitor alarms after the patient was assessed during morning rounds and found not to have any acute issues. It was hoped that this decision would eliminate the distraction of the nuisance alarms. However, during the patient’s routine afternoon assessment, the rounding physician noted cold and diaphoretic extremities with markedly increased swelling. Interrogation of the monitor system revealed progressive bradycardia and hypotension over the past hour. An emergency CT angiogram showed a massive pulmonary embolism, prompting immediate thrombolytic therapy and patient transfer to intensive care. Despite aggressive management, the patient’s shock became refractory, culminating in his death several hours later.
\n
\n
\n
5.4. Summation of Clinical Vignettes: finding common threads
\n
The three hypothetical clinical scenarios outlined above share a common theme: dedicated monitoring systems implemented to ensure early detection of clinical deterioration and thus patient safety were utilized either ineffectively or incorrectly. In all three vignettes, a confluence of factors (environment, patient, medical personnel) subsequently led to AF and then adverse patient outcomes. In the following sections, we will further discuss the phenomenon of alarm fatigue, focusing on its impact on daily clinical practice.
\n
\n
\n
\n
6. Alarm fatigue
\n
After the general introduction of AF earlier in the chapter, the authors will now discuss this important concept in greater detail. The phenomenon of AF is multifaceted and includes increased clinician response time with simultaneous decreased response rate that is mainly attributed to excessive stimuli from clinical alarms [8]. Depending on patient acuity and clinical monitoring requirements, typical bedside health-care personnel may be exposed to as many as 1000 alarms during a single shift, of which as many as 95% can be nonactionable and thus do not require immediate clinical determination [8, 66, 67]. Given the multitude of clinical alarms, a provider has to sort through during a typical hospital shift, there will be a natural tendency to potentially dismiss certain alarms as insignificant through rationalization. This phenomenon is described in the literature as the natural human behavioral reaction to “deprioritize signals” that have often been proven to be either false or misleading. Thus, staff may begin reflexively disabling or silencing alarm systems, which could effectively mask other alarms that may be clinically significant [68, 69]. To some extent, this behavioral pattern was seen in all three Clinical Vignettes, where the actionable alarm was masked by the vast number of nonactionable alarms that preceded it. Ultimately, the resulting delay in response or inadequate response puts patient safety at risk and may result in morbidity and/or mortality [70, 71]. Technologically advanced physiologic monitors bring a lot of promise, both in terms of earlier and more sensitive detection of patient deterioration (or other clinically significant event); however, the sensory overload and desensitization associated with AF will likely continue to present a major opportunity for improvement.
\n
Certain other factors have been implicated in the increased incidence and severity of alarm fatigue, including greater staff workload, higher patient acuity, and the complexity of the modern health-care environment [10]. Nurses serve as key frontline staff in most clinical settings and play a pivotal role in overseeing patient care and monitoring. Moreover, nurses are subject to significant occupational stress that can be attributed to multiple causes, including heavy workloads [72]. This stress, as outlined in previous sections of this chapter, certainly influences AF by forcing nurses to instantaneously adjust their work activities (and priorities) according to perceived importance of near constant clinical alarm activity. Our Clinical Vignette #2 illustrated the difficult task of ongoing patient triage, with the nurse having to prioritize between the three newly admitted patients and all of her other assigned patients. This constant need for clinical vigilance and prioritization is potentially disruptive to typical workflow, especially when high task complexity is involved. It can also contribute to the development of burnout [73]. Nurses have expressed the internal conflict between having to ignore the constant alarms simply to maintain sufficient focus to finish their routine tasks [74]. It is not surprising that increasing workload or task complexity has been associated with both suboptimal job performance and inconsistent alarm response [10]. Furthermore, the effort of acknowledging, evaluating, and responding to an alarm significantly increases the overall time commitment and workload of the nurses, which further perpetuates the trend of decreased alarm response and task performance [8].
\n
Because multiple factors contribute to AF, many existing models struggle to fully account for (and address) clinician behavioral patterns seen with AF [75]. At the same time, it should be noted that AF is not unique to clinicians. In fact, a similar phenomenon has also been seen among human operators utilizing automated monitoring systems, such as aircraft pilots and nuclear power plant operators. The excessive number of alarm activations leads to the tendency of operators to ignore alerts, particularly when the monitoring system produces a high rate of false alarms or alerts [75]. For these operational environments, it has also been suggested that increased primary and secondary task workloads have a compounding effect on alarm response degradation that may occur in the setting of low alarm system reliability [76]. Similar to the clinical setting, AF can be associated with serious safety risks and represents a similar barrier to the practical application of automated monitoring systems in other fields (Figure 3).
\n
Figure 3.
The word cloud demonstrating the multifaceted phenomenon of alarm fatigue.
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\n
\n
7. Potential outcomes of alarm fatigue
\n
Significant percentage of nonactionable alarms in the typical modern clinical environment can lead to the development (and subsequent habituation) of AF. As previously mentioned, AF can be characterized by alarm desensitization, mistrust of alert accuracy/utility, and delay of caregiver response (or even lack thereof). Commonly seen reactions to AF include the deactivation and silencing of systems or adjustment of alarm parameters to decrease the number of alarms. Such reactive behaviors have the potential to result in missed critical alarms, leading to patient morbidity or even mortality. In fact, patient safety considerations associated with AF are among the top items of Emergency Care Research (ECRI) Institute’s Health Technology Hazards list [77, 78]. The subject of AF has been extensively studied, primarily due to its high prevalence across essentially all health-care settings. The underreporting of alarm-related events has been recognized as a challenge, and it should be noted that recorded incidents likely reflect only a small proportion of actual events. Available records from the Joint Commission’s Sentinel Event Database show 98 alarm-related occurrences between January 2009 and June 2012 (Figure 4). Of these reported events, several common alarm system issues (Figure 5) were directly connected to events leading to injury or death (Table 1) [79].
\n
Figure 4.
Alarm-related events and subsequent results from January 2009 to June 2012 (source: Joint Commission’s Sentinel Event Database).
\n
Figure 5.
Major contributing factors of alarm-related events (source: Joint Commission’s Sentinel Event Database).
\n
\n
\n\n
\n
Event
\n
\n\n\n
\n
Falls
\n
\n
\n
Delays in treatment
\n
\n
\n
Delays in ventilator use
\n
\n
\n
Medication errors
\n
\n\n
Table 1.
Common alarm-related events leading to injuries or deaths.
Additionally, the US Food and Drug Administration’s Manufacturer and User Facility Device Experience (MAUDE) database has identified 566 alarm-related patient deaths between January 2005 and June 2010 [79]. Reports detailing alarm-related events have prompted thorough investigation into AF and possible strategies to address this important phenomenon in the clinical setting.
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8. Quality improvement
\n
Considering the potential for very serious clinical consequences of AF, quality improvement measures have been proposed to help reduce both nonactionable alarm occurrences and the incidence of AF. Successful quality improvement projects must address multiple facets of the overall problem, including root causes that lead to AF (Figure 6). For example, poor usability and lack of user-centered devices have the potential for elevating clinical personnel stress levels, creating unnecessary workload and interjecting workflow inefficiencies into an already tense environment [81].
\n
Figure 6.
The different aspects of alarm fatigue that can be addressed through different quality improvement approaches (source: Ref. [80]).
\n
Potential solutions for reducing the incidence of AF include multipronged approaches consisting of staff education, equipment (hardware and software) enhancements, and implementation of more efficient clinical protocols or guidelines [82, 83, 84]. From an educational perspective, it is important to ensure adequate staff education, equipment training, and closer team collaboration to improve patient safety within the existing framework [8, 85]. In addition to staff education, hospital policies have been developed and implemented to more clearly define which staff members are able to change alarm settings, as well as how such changes should be made and documented. Many of these polices have also delegated the responsibility of performing clinical alarm monitoring rounds to a staff member in order to allow for continued review of the application of patient monitoring systems [86, 87, 88].
\n
To address the issues of staff workload, two potential approaches have been proposed. The first approach consists of secondary notification systems. The second option involves the use of dedicated staff to oversee alarms. A secondary notification system involves a specialized network interface that algorithmically facilitates the decision process regarding which alarms will be further communicated or escalated to pertinent downstream clinical staff. Further, this system would also enable the automatic escalation of an alert to another clinician, should the primary recipient fail to acknowledge the alarm within a designated timeframe. The use of staff to oversee alarms, while an expensive option, can give additional support to care providers in the form of dedicated personnel whose responsibility is to continuously monitor patient data trends and alarms from a central station [58].
\n
No matter the solution, all the quality improvement processes require a multidisciplinary approach to address the causes and effects of AF. Only through collaborative efforts can substantial change be accomplished to reduce the number of alarm-related events in health care. In addition to the quality improvement measures taken by hospitals, technological advances have also led to more efficient and practical application of patient monitors in the clinical setting. These advances are directed at the reduction of nonactionable alarms with the goal of decreasing the alarm desensitization associated with AF. The importance of adequate information technology support, including better device designs, must be emphasized. As increasingly efficient and complex monitoring equipment is introduced into the clinical realm, certain phenomena, such as the emergence of “unpredictable code,” may adversely affect computer performance (including the ability to effectively recognize important data patterns) and lead to clinical alerts being missed despite the fact that alert-specific data were clearly and provably present [89].
\n
\n
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9. Technological advances in patient monitors
\n
In general, clinical monitoring is based on a careful balance between sensitivity and specificity of alarm signal recognition, as well as the associated threshold setting required to trigger “alert condition” [90, 91]. Increasing monitor sensitivity helps ensure that truly significant events are not missed, primarily using single-parameter alarms and default thresholds [8]. However, as a trade-off this increases the incidence of nuisance alarms that are nonactionable. This issue may be remedied by the development of “smart alarm systems” that use algorithmic approaches to evaluate multiple parameters prior to determining whether the detected change is truly critical, and only then sending an alert to the operator [15]. This improvement in device specificity would result in significantly fewer false alarms and therefore reduce AF. At the same time, the challenges of “unpredictable code” and “interrupted or corrupt data” have been noted and may represent an important safety issue due to the potential for missing data or data misinterpretation, especially when using memory-intensive applications on devices that are continually operating for prolonged periods of time [89, 92, 93, 94, 95].
\n
The ideal patient monitor would have high sensitivity, as well as high negative predictive value for life-threatening clinical scenarios. This would result in excellent “event detection rate” while reducing the number of false and nuisance alarms. Still, any improvement of sensitivity/negative predicative value for monitors must be accompanied by corresponding adjustment to specificity/positive predictive value, ensuring that clinically significant events are captured efficiently [33]. The accomplishment of the above goals may be possible using the application of artificial intelligence (AI) in monitoring systems, wherein AI would be incorporated into logic-based, decision-making systems. The ultimate goal would be the development of clinical monitoring capabilities that reflect and mirror human cognitive/decision-making processes [37]. In the context of this chapter’s Clinical Vignettes, the application of such AI-based systems might be helpful in minimizing the number of nonactionable alarms, thus reducing the subsequent AF associated with adverse clinical events. So far, the utilization of AI has been explored in several different applications (Table 2).
\n
\n
\n
\n
\n\n
\n
System
\n
Description
\n
Application
\n
\n\n\n
\n
Rule-based expert systems
\n
Application of expert knowledge from a compiled database to new context and simulation of expert decisions
\n
Development of a highly specific patient monitor system with electronic access to data available in a multichannel patient monitor and data management system to detect cardiac disturbances [37, 96]
\n
\n
\n
Neural networks
\n
Utilization of artificial neural networks to predict disease presence based on advanced information
\n
Development of neuronal network used to detect myocardial infarction early on in patients admitted for chest pain [37, 97]
\n
\n
\n
Fuzzy logic
\n
Diffuse processing of exact data that does not indicate an explicit conclusion
\n
Development of a monitor system able to diagnose simulated cardiac arrest via evaluation of EKG, capnography, and arterial blood pressure [37, 98]
\n
\n
\n
Bayesian networks
\n
System used for the estimation of event occurrence based on causal probabilistic networks
\n
Application of system for decision support in cardiac event detection [37, 99]
\n
\n\n
Table 2.
Applications of artificial intelligence in the development of intensive care monitoring.
Given the proliferation of advanced monitoring equipment, AF continues to be a major patient safety issue across modern health-care systems. While technological advances show great promise in improving patient care, significant barriers to more optimal implementations exist, including the ongoing struggle to balance the need for high sensitivity versus the excessive number nonactionable clinical alarms. The high frequency of clinical alerts, especially when combined with heavy clinical workload, is known to have negative effects of hospital staff, including alarm desensitization and subsequent delay and/or lack of caregiver response. The resultant AF poses a serious risk to patient safety and has been associated with significant adverse events, including the need for additional or prolonged hospital care, excess attributable morbidity, and even mortality. Prevention of AF requires a multipronged approach consisting of quality improvement measures, staff training, better equipment management (e.g., monitor threshold adjustments) to reduce false alarms, and focus on optimizing staff workload.
\n
\n\n',keywords:"alarm fatigue, clinical alarms, clinical monitoring, monitoring equipment, patient safety",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/66118.pdf",chapterXML:"https://mts.intechopen.com/source/xml/66118.xml",downloadPdfUrl:"/chapter/pdf-download/66118",previewPdfUrl:"/chapter/pdf-preview/66118",totalDownloads:626,totalViews:0,totalCrossrefCites:0,dateSubmitted:"October 15th 2018",dateReviewed:"January 28th 2019",datePrePublished:"March 12th 2019",datePublished:"September 18th 2019",dateFinished:null,readingETA:"0",abstract:"As the demand for health-care services continues to increase, clinically efficient and cost-effective patient monitoring takes on a critically important role. Key considerations inherent to this area of concern include patient safety, reliability, ease of use, and cost containment. Unfortunately, even the most modern patient monitoring systems carry significant drawbacks that limit their effectiveness and/or applicability. Major opportunities for improvement in both equipment design and monitor utilization have been identified, including the presence of excessive false and nuisance alarms. When poorly optimized, clinical alarm activity can affect patient safety and may have a negative impact on care providers, leading to inappropriate alarm response time due to the so-called alarm fatigue (AF). Ultimately, consequences of AF include missed alerts of clinical significance, with substantial risk for patient harm and potentially fatal outcomes. Targeted quality improvement initiatives and staff training, as well as the proactive incorporation of technological improvements, are the best approaches to address key barriers to the optimal utilization of clinical alarms, AF reduction, better patient care, and improved provider job satisfaction.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/66118",risUrl:"/chapter/ris/66118",signatures:"James Nguyen, Kendra Davis, Giuseppe Guglielmello and Stanislaw P. Stawicki",book:{id:"7447",title:"Vignettes in Patient Safety",subtitle:"Volume 4",fullTitle:"Vignettes in Patient Safety - Volume 4",slug:"vignettes-in-patient-safety-volume-4",publishedDate:"September 18th 2019",bookSignature:"Stanislaw P. Stawicki and Michael S. Firstenberg",coverURL:"https://cdn.intechopen.com/books/images_new/7447.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"181694",title:"Dr.",name:"Stanislaw P.",middleName:null,surname:"Stawicki",slug:"stanislaw-p.-stawicki",fullName:"Stanislaw P. Stawicki"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"181694",title:"Dr.",name:"Stanislaw P.",middleName:null,surname:"Stawicki",fullName:"Stanislaw P. Stawicki",slug:"stanislaw-p.-stawicki",email:"stawicki.ace@gmail.com",position:null,institution:{name:"St. Luke's University Health Network",institutionURL:null,country:{name:"United States of America"}}}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Primary research methods",level:"1"},{id:"sec_3",title:"3. Patient monitoring: different types and modalities",level:"1"},{id:"sec_3_2",title:"3.1. Ventilation/oxygenation alarms",level:"2"},{id:"sec_4_2",title:"3.2. Hemodynamic alarms",level:"2"},{id:"sec_5_2",title:"3.3. Bed and chair pressure sensors",level:"2"},{id:"sec_7",title:"4. Patient monitor alarm design",level:"1"},{id:"sec_8",title:"5. Clinical Vignettes",level:"1"},{id:"sec_8_2",title:"5.1. Clinical Vignette #1: 62-year-old female presenting with chronic obstructive pulmonary disease (COPD) exacerbation",level:"2"},{id:"sec_9_2",title:"5.2. Clinical Vignette #2: 65-year-old male transferred to inpatient unit following a total knee arthroplasty",level:"2"},{id:"sec_10_2",title:"5.3. Clinical Vignette #3: 71-year-old male with history of multiple myeloma admitted for right lower extremity swelling associated with minor pain",level:"2"},{id:"sec_11_2",title:"5.4. Summation of Clinical Vignettes: finding common threads",level:"2"},{id:"sec_13",title:"6. Alarm fatigue",level:"1"},{id:"sec_14",title:"7. Potential outcomes of alarm fatigue",level:"1"},{id:"sec_15",title:"8. Quality improvement",level:"1"},{id:"sec_16",title:"9. Technological advances in patient monitors",level:"1"},{id:"sec_17",title:"10. Conclusion",level:"1"}],chapterReferences:[{id:"B1",body:'Kesselheim AS et al. Clinical decision support systems could be modified to reduce ‘alert fatigue’ while still minimizing the risk of litigation. Health Affairs. 2011;30(12):2310-2317\n'},{id:"B2",body:'Oppenheim MI et al. Design of a clinical alert system to facilitate development, testing, maintenance, and user-specific notification. In: Proceedings of the AMIA Symposium. Bethesda, Maryland: American Medical Informatics Association; 2000\n'},{id:"B3",body:'Konkani A, Oakley B, Bauld TJ. Reducing hospital noise: A review of medical device alarm management. 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Exploring measurement biases associated with esophageal Doppler monitoring in critically ill patients in intensive care unit. Annals of Thoracic Medicine. 2007;2(4):148\n'},{id:"B45",body:'Kelly N et al. Clinician-performed ultrasound in hemodynamic and cardiac assessment: A synopsis of current indications and limitations. European Journal of Trauma and Emergency Surgery. 2015;41(5):469-480\n'},{id:"B46",body:'Stawicki PS et al. Use of the esophageal echo-Doppler to guide intensive care unit resuscitations: A retrospective study. Indian Journal of Critical Care Medicine. 2007;11(2):54\n'},{id:"B47",body:'Kwok T et al. Does access to bed-chair pressure sensors reduce physical restraint use in the rehabilitative care setting? Journal of Clinical Nursing. 2006;15(5):581-587\n'},{id:"B48",body:'Sahota O et al. REFINE (REducing falls in In-patieNt Elderly) using bed and bedside chair pressure sensors linked to radio-pagers in acute hospital care: A randomised controlled trial. Age and Ageing. 2013;43(2):247-253\n'},{id:"B49",body:'Hitcho EB et al. Characteristics and circumstances of falls in a hospital setting: A prospective analysis. Journal of General Internal Medicine. 2004;19(7):732-739\n'},{id:"B50",body:'El-Bendary N et al. Fall detection and prevention for the elderly: A review of trends and challenges. International Journal on Smart Sensing & Intelligent Systems. 2013;6(3):1-37\n'},{id:"B51",body:'Abbate S et al. Recognition of False Alarms in Fall Detection Systems. In: 2011 IEEE Consumer Communications and Networking Conference (CCNC). Las Vegas, Nevada: IEEE; 9 Jan 2011. pp. 23-28\n'},{id:"B52",body:'Shorr RI et al. Effects of an intervention to increase bed alarm use to prevent falls in hospitalized patients: A cluster randomized trial. Annals of Internal Medicine. 2012;157(10):692-699\n'},{id:"B53",body:'Cuthbertson BH. Optimising early warning scoring systems. Resuscitation. 2008;77(2):153-154\n'},{id:"B54",body:'Talley LB et al. Cardiopulmonary monitors and clinically significant events in critically ill children. Biomedical Instrumentation & Technology. 2011;45(s1):38-45\n'},{id:"B55",body:'Poets C, Stebbens V. Detection of movement artifact in recorded pulse oximeter saturation. European Journal of Pediatrics. 1997;156(10):808-811\n'},{id:"B56",body:'Weinger MB, Smith NT. Vigilance, Alarms, and Integrated Monitoring Systems. Anesthesia Equipment: Principles and Applications. Malvern, PA: Mosby Year Book; 1993. pp. 350-384\n'},{id:"B57",body:'Barach P, Sanchez JA. Redesigning hospital alarms for reliable and safe care. In: Surgical Patient Care. Cham, Switzerland: Springer; 2017. pp. 263-275\n'},{id:"B58",body:'Karnik A, Bonafide CP. A framework for reducing alarm fatigue on pediatric inpatient units. Hospital Pediatrics. 2015;5(3):160-163\n'},{id:"B59",body:'Kalisch BJ, Aebersold M. Interruptions and multitasking in nursing care. The Joint Commission Journal on Quality and Patient Safety. 2010;36(3):126-132\n'},{id:"B60",body:'Nolan TW. System changes to improve patient safety. BMJ: British Medical Journal. 2000;320(7237):771\n'},{id:"B61",body:'Laxmisan A et al. The multitasking clinician: Decision-making and cognitive demand during and after team handoffs in emergency care. International Journal of Medical Informatics. 2007;76(11-12):801-811\n'},{id:"B62",body:'Hussain M, Dewey J, Weibel N. Reducing alarm fatigue: exploring decision structures, risks, and design. EAI Endorsed Transactions on Pervasive Health and Technology. 2017;3:1-4\n'},{id:"B63",body:'Moffatt-Bruce SD, Huerta T. From ideas to institute for the design of environments aligned for patient safety (IDEA4PS): The reality of research and the keys to making it work. International Journal of Academic Medicine. 2016;2(3):2\n'},{id:"B64",body:'Edworthy J. Medical audible alarms: A review. Journal of the American Medical Informatics Association. 2012;20(3):584-589\n'},{id:"B65",body:'Lacherez P, Seah EL, Sanderson P. Overlapping melodic alarms are almost indiscriminable. Human Factors. 2007;49(4):637-645\n'},{id:"B66",body:'Gorges M, Markewitz BA, Westenskow DR. Improving alarm performance in the medical intensive care unit using delays and clinical context. Anesthesia and Analgesia. 2009;108(5):1546-1552\n'},{id:"B67",body:'Tsien CL, Fackler JC. Poor prognosis for existing monitors in the intensive care unit. Critical Care Medicine. 1997;25(4):614-619\n'},{id:"B68",body:'Phillips J, Barnsteiner JH. Clinical alarms: Improving efficiency and effectiveness. Critical Care Nursing Quarterly. 2005;28(4):317-323\n'},{id:"B69",body:'Varpio L et al. The helpful or hindering effects of in-hospital patient monitor alarms on nurses: A qualitative analysis. CIN: Computers, Informatics, Nursing. 2012;30(4):210-217\n'},{id:"B70",body:'Bridi AC, Louro TQ, da Silva RC. Clinical alarms in intensive care: Implications of alarm fatigue for the safety of patients. Revista Latino-Americana de Enfermagem. 2014;22(6):1034-1040\n'},{id:"B71",body:'Edworthy J. Alarms are still a problem! Anaesthesia. 2013;68(8):791-794\n'},{id:"B72",body:'Happell B et al. Nurses and stress: Recognizing causes and seeking solutions. Journal of Nursing Management. 2013;21(4):638-647\n'},{id:"B73",body:'Tolentino JC et al. What\'s new in academic medicine: Can we effectively address the burnout epidemic in healthcare? International Journal of Academic Medicine. 2017;3(3):1\n'},{id:"B74",body:'Solvoll T, Arntsen H, Hartvigsen G. Alarm fatigue vs user expectations regarding context-aware alarm handling in hospital environments using CallMeSmart. Studies in Health Technology and Informatics. 2017;241:159-164\n'},{id:"B75",body:'Bailey JM. The implications of probability matching for clinician response to vital sign alarms: A theoretical study of alarm fatigue. 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A knowledge-based alarm system for monitoring cardiac operated patients—Technical construction and evaluation. International Journal of Clinical Monitoring and Computing. 1993;10(2):117-126\n'},{id:"B97",body:'Baxt WG, Skora J. Prospective validation of artificial neural network trained to identify acute myocardial infarction. Lancet. 1996;347(8993):12-15\n'},{id:"B98",body:'Goldman JM, Cordova MJ. Advanced clinical monitoring: Considerations for real-time hemodynamic diagnostics. In: Proceedings of the Annual Symposium on Computer Applications in Medical Care. 1994. pp. 752-756\n'},{id:"B99",body:'Laursen P. Event detection on patient monitoring data using causal probabilistic networks. Methods of Information in Medicine. 1994;33(1):111-115\n'}],footnotes:[],contributors:[{corresp:null,contributorFullName:"James Nguyen",address:null,affiliation:'
Medical School of Temple University/St. Luke’s University Health Network, USA
Department of Medicine, Section of Pulmonary and Critical Care Medicine, St. Luke’s University Health Network, USA
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Without well-managed tourism, the fireflies have faced to the problems of shooting camera flashes from tourists. Although the effect of artificial light was well understood, which causes negative impact to firefly courtship, there is no obvious information on the effect of the camera illumination. The experiment of testing four types of camera illumination was set up in laboratory using wild populations of P. malaccae. The flash patterns were recorded by videotaping and analyzed by using TiLIA software. The results showed that all kinds of camera illuminations affect flashing behavior of the fireflies. They prolonged flash interval by increasing pulse duration. 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UK Research and Innovation (former Research Councils UK (RCUK) - including AHRC, BBSRC, ESRC, EPSRC, MRC, NERC, STFC.) Processing charges for books/book chapters can be covered through RCUK block grants which are allocated to most universities in the UK, which then handle the OA publication funding requests. It is at the discretion of the university whether it will approve the request.)
UK Research and Innovation (former Research Councils UK (RCUK) - including AHRC, BBSRC, ESRC, EPSRC, MRC, NERC, STFC.) Processing charges for books/book chapters can be covered through RCUK block grants which are allocated to most universities in the UK, which then handle the OA publication funding requests. It is at the discretion of the university whether it will approve the request.)
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