2,957 research outputs found

    Mobile Device Background Sensors: Authentication vs Privacy

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    The increasing number of mobile devices in recent years has caused the collection of a large amount of personal information that needs to be protected. To this aim, behavioural biometrics has become very popular. But, what is the discriminative power of mobile behavioural biometrics in real scenarios? With the success of Deep Learning (DL), architectures based on Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), such as Long Short-Term Memory (LSTM), have shown improvements compared to traditional machine learning methods. However, these DL architectures still have limitations that need to be addressed. In response, new DL architectures like Transformers have emerged. The question is, can these new Transformers outperform previous biometric approaches? To answers to these questions, this thesis focuses on behavioural biometric authentication with data acquired from mobile background sensors (i.e., accelerometers and gyroscopes). In addition, to the best of our knowledge, this is the first thesis that explores and proposes novel behavioural biometric systems based on Transformers, achieving state-of-the-art results in gait, swipe, and keystroke biometrics. The adoption of biometrics requires a balance between security and privacy. Biometric modalities provide a unique and inherently personal approach for authentication. Nevertheless, biometrics also give rise to concerns regarding the invasion of personal privacy. According to the General Data Protection Regulation (GDPR) introduced by the European Union, personal data such as biometric data are sensitive and must be used and protected properly. This thesis analyses the impact of sensitive data in the performance of biometric systems and proposes a novel unsupervised privacy-preserving approach. The research conducted in this thesis makes significant contributions, including: i) a comprehensive review of the privacy vulnerabilities of mobile device sensors, covering metrics for quantifying privacy in relation to sensitive data, along with protection methods for safeguarding sensitive information; ii) an analysis of authentication systems for behavioural biometrics on mobile devices (i.e., gait, swipe, and keystroke), being the first thesis that explores the potential of Transformers for behavioural biometrics, introducing novel architectures that outperform the state of the art; and iii) a novel privacy-preserving approach for mobile biometric gait verification using unsupervised learning techniques, ensuring the protection of sensitive data during the verification process

    Prediction of stroke patients’ bedroom-stay duration: machine-learning approach using wearable sensor data

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    Background: The importance of being physically active and avoiding staying in bed has been recognized in stroke rehabilitation. However, studies have pointed out that stroke patients admitted to rehabilitation units often spend most of their day immobile and inactive, with limited opportunities for activity outside their bedrooms. To address this issue, it is necessary to record the duration of stroke patients staying in their bedrooms, but it is impractical for medical providers to do this manually during their daily work of providing care. Although an automated approach using wearable devices and access points is more practical, implementing these access points into medical facilities is costly. However, when combined with machine learning, predicting the duration of stroke patients staying in their bedrooms is possible with reduced cost. We assessed using machine learning to estimate bedroom-stay duration using activity data recorded with wearable devices.Method: We recruited 99 stroke hemiparesis inpatients and conducted 343 measurements. Data on electrocardiograms and chest acceleration were measured using a wearable device, and the location name of the access point that detected the signal of the device was recorded. We first investigated the correlation between bedroom-stay duration measured from the access point as the objective variable and activity data measured with a wearable device and demographic information as explanatory variables. To evaluate the duration predictability, we then compared machine-learning models commonly used in medical studies.Results: We conducted 228 measurements that surpassed a 90% data-acquisition rate using Bluetooth Low Energy. Among the explanatory variables, the period spent reclining and sitting/standing were correlated with bedroom-stay duration (Spearman’s rank correlation coefficient (R) of 0.56 and −0.52, p < 0.001). Interestingly, the sum of the motor and cognitive categories of the functional independence measure, clinical indicators of the abilities of stroke patients, lacked correlation. The correlation between the actual bedroom-stay duration and predicted one using machine-learning models resulted in an R of 0.72 and p < 0.001, suggesting the possibility of predicting bedroom-stay duration from activity data and demographics.Conclusion: Wearable devices, coupled with machine learning, can predict the duration of patients staying in their bedrooms. Once trained, the machine-learning model can predict without continuously tracking the actual location, enabling more cost-effective and privacy-centric future measurements

    Design and Evaluation of a Hardware System for Online Signal Processing within Mobile Brain-Computer Interfaces

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    Brain-Computer Interfaces (BCIs) sind innovative Systeme, die eine direkte Kommunikation zwischen dem Gehirn und externen Geräten ermöglichen. Diese Schnittstellen haben sich zu einer transformativen Lösung nicht nur für Menschen mit neurologischen Verletzungen entwickelt, sondern auch für ein breiteres Spektrum von Menschen, das sowohl medizinische als auch nicht-medizinische Anwendungen umfasst. In der Vergangenheit hat die Herausforderung, dass neurologische Verletzungen nach einer anfänglichen Erholungsphase statisch bleiben, die Forscher dazu veranlasst, innovative Wege zu beschreiten. Seit den 1970er Jahren stehen BCIs an vorderster Front dieser Bemühungen. Mit den Fortschritten in der Forschung haben sich die BCI-Anwendungen erweitert und zeigen ein großes Potenzial für eine Vielzahl von Anwendungen, auch für weniger stark eingeschränkte (zum Beispiel im Kontext von Hörelektronik) sowie völlig gesunde Menschen (zum Beispiel in der Unterhaltungsindustrie). Die Zukunft der BCI-Forschung hängt jedoch auch von der Verfügbarkeit zuverlässiger BCI-Hardware ab, die den Einsatz in der realen Welt gewährleistet. Das im Rahmen dieser Arbeit konzipierte und implementierte CereBridge-System stellt einen bedeutenden Fortschritt in der Brain-Computer-Interface-Technologie dar, da es die gesamte Hardware zur Erfassung und Verarbeitung von EEG-Signalen in ein mobiles System integriert. Die Architektur der Verarbeitungshardware basiert auf einem FPGA mit einem ARM Cortex-M3 innerhalb eines heterogenen ICs, was Flexibilität und Effizienz bei der EEG-Signalverarbeitung gewährleistet. Der modulare Aufbau des Systems, bestehend aus drei einzelnen Boards, gewährleistet die Anpassbarkeit an unterschiedliche Anforderungen. Das komplette System wird an der Kopfhaut befestigt, kann autonom arbeiten, benötigt keine externe Interaktion und wiegt einschließlich der 16-Kanal-EEG-Sensoren nur ca. 56 g. Der Fokus liegt auf voller Mobilität. Das vorgeschlagene anpassbare Datenflusskonzept erleichtert die Untersuchung und nahtlose Integration von Algorithmen und erhöht die Flexibilität des Systems. Dies wird auch durch die Möglichkeit unterstrichen, verschiedene Algorithmen auf EEG-Daten anzuwenden, um unterschiedliche Anwendungsziele zu erreichen. High-Level Synthesis (HLS) wurde verwendet, um die Algorithmen auf das FPGA zu portieren, was den Algorithmenentwicklungsprozess beschleunigt und eine schnelle Implementierung von Algorithmusvarianten ermöglicht. Evaluierungen haben gezeigt, dass das CereBridge-System in der Lage ist, die gesamte Signalverarbeitungskette zu integrieren, die für verschiedene BCI-Anwendungen erforderlich ist. Darüber hinaus kann es mit einer Batterie von mehr als 31 Stunden Dauerbetrieb betrieben werden, was es zu einer praktikablen Lösung für mobile Langzeit-EEG-Aufzeichnungen und reale BCI-Studien macht. Im Vergleich zu bestehenden Forschungsplattformen bietet das CereBridge-System eine bisher unerreichte Leistungsfähigkeit und Ausstattung für ein mobiles BCI. Es erfüllt nicht nur die relevanten Anforderungen an ein mobiles BCI-System, sondern ebnet auch den Weg für eine schnelle Übertragung von Algorithmen aus dem Labor in reale Anwendungen. Im Wesentlichen liefert diese Arbeit einen umfassenden Entwurf für die Entwicklung und Implementierung eines hochmodernen mobilen EEG-basierten BCI-Systems und setzt damit einen neuen Standard für BCI-Hardware, die in der Praxis eingesetzt werden kann.Brain-Computer Interfaces (BCIs) are innovative systems that enable direct communication between the brain and external devices. These interfaces have emerged as a transformative solution not only for individuals with neurological injuries, but also for a broader range of individuals, encompassing both medical and non-medical applications. Historically, the challenge of neurological injury being static after an initial recovery phase has driven researchers to explore innovative avenues. Since the 1970s, BCIs have been at one forefront of these efforts. As research has progressed, BCI applications have expanded, showing potential in a wide range of applications, including those for less severely disabled (e.g. in the context of hearing aids) and completely healthy individuals (e.g. entertainment industry). However, the future of BCI research also depends on the availability of reliable BCI hardware to ensure real-world application. The CereBridge system designed and implemented in this work represents a significant leap forward in brain-computer interface technology by integrating all EEG signal acquisition and processing hardware into a mobile system. The processing hardware architecture is centered around an FPGA with an ARM Cortex-M3 within a heterogeneous IC, ensuring flexibility and efficiency in EEG signal processing. The modular design of the system, consisting of three individual boards, ensures adaptability to different requirements. With a focus on full mobility, the complete system is mounted on the scalp, can operate autonomously, requires no external interaction, and weighs approximately 56g, including 16 channel EEG sensors. The proposed customizable dataflow concept facilitates the exploration and seamless integration of algorithms, increasing the flexibility of the system. This is further underscored by the ability to apply different algorithms to recorded EEG data to meet different application goals. High-Level Synthesis (HLS) was used to port algorithms to the FPGA, accelerating the algorithm development process and facilitating rapid implementation of algorithm variants. Evaluations have shown that the CereBridge system is capable of integrating the complete signal processing chain required for various BCI applications. Furthermore, it can operate continuously for more than 31 hours with a 1800mAh battery, making it a viable solution for long-term mobile EEG recording and real-world BCI studies. Compared to existing research platforms, the CereBridge system offers unprecedented performance and features for a mobile BCI. It not only meets the relevant requirements for a mobile BCI system, but also paves the way for the rapid transition of algorithms from the laboratory to real-world applications. In essence, this work provides a comprehensive blueprint for the development and implementation of a state-of-the-art mobile EEG-based BCI system, setting a new benchmark in BCI hardware for real-world applicability

    Active and assisted living ecosystem for the elderly

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    A novel ecosystem to promote the physical, emotional and psychic health and well-being of the elderly is presented. Our proposal was designed to add several services developed to meet the needs of the senior population, namely services to improve social inclusion and increase contribution to society. Moreover, the solution monitors the vital signs of elderly individuals, as well as environmental parameters and behavior patterns, in order to seek eminent danger situations and predict potential hazardous issues, acting in accordance with the various alert levels specified for each individual. The platform was tested by seniors in a real scenario. The experimental results demonstrated that the proposed ecosystem was well accepted and is easy to use by seniors

    Deep Learning Techniques for Electroencephalography Analysis

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    In this thesis we design deep learning techniques for training deep neural networks on electroencephalography (EEG) data and in particular on two problems, namely EEG-based motor imagery decoding and EEG-based affect recognition, addressing challenges associated with them. Regarding the problem of motor imagery (MI) decoding, we first consider the various kinds of domain shifts in the EEG signals, caused by inter-individual differences (e.g. brain anatomy, personality and cognitive profile). These domain shifts render multi-subject training a challenging task and impede robust cross-subject generalization. We build a two-stage model ensemble architecture and propose two objectives to train it, combining the strengths of curriculum learning and collaborative training. Our subject-independent experiments on the large datasets of Physionet and OpenBMI, verify the effectiveness of our approach. Next, we explore the utilization of the spatial covariance of EEG signals through alignment techniques, with the goal of learning domain-invariant representations. We introduce a Riemannian framework that concurrently performs covariance-based signal alignment and data augmentation, while training a convolutional neural network (CNN) on EEG time-series. Experiments on the BCI IV-2a dataset show that our method performs superiorly over traditional alignment, by inducing regularization to the weights of the CNN. We also study the problem of EEG-based affect recognition, inspired by works suggesting that emotions can be expressed in relative terms, i.e. through ordinal comparisons between different affective state levels. We propose treating data samples in a pairwise manner to infer the ordinal relation between their corresponding affective state labels, as an auxiliary training objective. We incorporate our objective in a deep network architecture which we jointly train on the tasks of sample-wise classification and pairwise ordinal ranking. We evaluate our method on the affective datasets of DEAP and SEED and obtain performance improvements over deep networks trained without the additional ranking objective

    Evaluation of Data Processing and Artifact Removal Approaches Used for Physiological Signals Captured Using Wearable Sensing Devices during Construction Tasks

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    Wearable sensing devices (WSDs) have enormous promise for monitoring construction worker safety. They can track workers and send safety-related information in real time, allowing for more effective and preventative decision making. WSDs are particularly useful on construction sites since they can track workers’ health, safety, and activity levels, among other metrics that could help optimize their daily tasks. WSDs may also assist workers in recognizing health-related safety risks (such as physical fatigue) and taking appropriate action to mitigate them. The data produced by these WSDs, however, is highly noisy and contaminated with artifacts that could have been introduced by the surroundings, the experimental apparatus, or the subject’s physiological state. These artifacts are very strong and frequently found during field experiments. So, when there is a lot of artifacts, the signal quality drops. Recently, artifacts removal has been greatly enhanced by developments in signal processing, which has vastly enhanced the performance. Thus, the proposed review aimed to provide an in-depth analysis of the approaches currently used to analyze data and remove artifacts from physiological signals obtained via WSDs during construction-related tasks. First, this study provides an overview of the physiological signals that are likely to be recorded from construction workers to monitor their health and safety. Second, this review identifies the most prevalent artifacts that have the most detrimental effect on the utility of the signals. Third, a comprehensive review of existing artifact-removal approaches were presented. Fourth, each identified artifact detection and removal approach was analyzed for its strengths and weaknesses. Finally, in conclusion, this review provides a few suggestions for future research for improving the quality of captured physiological signals for monitoring the health and safety of construction workers using artifact removal approaches

    Risk and threat mitigation techniques in internet of things (IoT) environments: a survey

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    Security in the Internet of Things (IoT) remains a predominant area of concern. Although several other surveys have been published on this topic in recent years, the broad spectrum that this area aims to cover, the rapid developments and the variety of concerns make it impossible to cover the topic adequately. This survey updates the state of the art covered in previous surveys and focuses on defences and mitigations against threats rather than on the threats alone, an area that is less extensively covered by other surveys. This survey has collated current research considering the dynamicity of the IoT environment, a topic missed in other surveys and warrants particular attention. To consider the IoT mobility, a life-cycle approach is adopted to the study of dynamic and mobile IoT environments and means of deploying defences against malicious actors aiming to compromise an IoT network and to evolve their attack laterally within it and from it. This survey takes a more comprehensive and detailed step by analysing a broad variety of methods for accomplishing each of the mitigation steps, presenting these uniquely by introducing a “defence-in-depth” approach that could significantly slow down the progress of an attack in the dynamic IoT environment. This survey sheds a light on leveraging redundancy as an inherent nature of multi-sensor IoT applications, to improve integrity and recovery. This study highlights the challenges of each mitigation step, emphasises novel perspectives, and reconnects the discussed mitigation steps to the ground principles they seek to implement

    Smart Gas Sensors: Materials, Technologies, Practical ‎Applications, and Use of Machine Learning – A Review

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    The electronic nose, popularly known as the E-nose, that combines gas sensor arrays (GSAs) with machine learning has gained a strong foothold in gas sensing technology. The E-nose designed to mimic the human olfactory system, is used for the detection and identification of various volatile compounds. The GSAs develop a unique signal fingerprint for each volatile compound to enable pattern recognition using machine learning algorithms. The inexpensive, portable and non-invasive characteristics of the E-nose system have rendered it indispensable within the gas-sensing arena. As a result, E-noses have been widely employed in several applications in the areas of the food industry, health management, disease diagnosis, water and air quality control, and toxic gas leakage detection. This paper reviews the various sensor fabrication technologies of GSAs and highlights the main operational framework of the E-nose system. The paper details vital signal pre-processing techniques of feature extraction, feature selection, in addition to machine learning algorithms such as SVM, kNN, ANN, and Random Forests for determining the type of gas and estimating its concentration in a competitive environment. The paper further explores the potential applications of E-noses for diagnosing diseases, monitoring air quality, assessing the quality of food samples and estimating concentrations of volatile organic compounds (VOCs) in air and in food samples. The review concludes with some challenges faced by E-nose, alternative ways to tackle them and proposes some recommendations as potential future work for further development and design enhancement of E-noses

    The association between pre-operative pain experience and post-operative pain in patients undergoing elective gastrointestinal surgery: a descriptive-comparative study

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    Aims: The purpose of the rapid review was to summarize and aggregate information for researchers and clinicians about predisposing factors for post-operative pain in laparoscopic patients and the prevalent management approaches post-operatively. The purpose of the descriptive-comparative study was to explore the associations between previous pain experiences and medication on the intensity of pain post-operatively in patients undergoing elective gastrointestinal surgery, using data collected by the Smart Pain Assessment Tool Based on Internet of Things. Methods: For the rapid review, the databases of PubMed, Web of Science and Embase were searched. ROBINS-I tool was used to evaluate the quality of non-randomized studies while ROB 2 tool was used for randomized controlled trials. For the descriptive-comparative study, 50 patients after gastrointestinal operations at Turku University hospital were included. The data collection of the study was done by a researcher belonging to Turku University staff at Turku University hospital. The data analysis was done by using descriptive and comparative methods of analysis. Descriptive statistics were used for the presentation and analysis of participants outcomes, diagnoses, procedures, and groupings based on variables related to the experience of pain (e.g., graphical measurement maximal pain levels using the numeric rating scale). Comparative statistics were used for associations and correlations regarding previous pain levels, medications, fear, and expectation of pain on maximal pain levels after gastrointestinal operations at Turku University Hospital. Results: The result of the rapid review suggest many predisposing factors for post-operative pain are influenced by the psychological profile of the patient. Among these factors are anxiety, fear, depression, expectation of pain, and other factors related to gastrointestinal surgery. Nevertheless, the results of this review also describe acute pre-operative pain, surgical factors, genetics, age, gender, obesity, and previous experiences of pain as relevant predisposing factors to pain following gastrointestinal surgery. Pain care strategies following gastrointestinal surgery include the use of pharmacological and non-pharmacological interventions. The literature suggests, non-pharmacological interventions are under-utilized and should be encouraged as an adjunct to pharmacological pain control strategies following elective gastrointestinal surgery. The results of the descriptive-comparative study somewhat contradict the results of the rapid review. Previous pain experiences or the recollection of preceding painful events were not associated with the administration of supplemental pain medication post-operatively (p = 0.741). Fear related to the upcoming pain following surgery was not associated with the level of invasiveness of the surgery (p = 0.662). In addition, the relationship between expectation of pain (p = 0.698), fear of pain related to the upcoming surgical procedure (p = 0.637) and medication post-operatively (p = .481) on the intensity of maximal post-operative pain was found to be negligible. The results of this study suggest patient expectation as a possible domain of intervention for better pain outcomes post-operatively. The administration of pain medication in the recovery room and the amount of pain medication in the recovery room were significant predictors of maximal post-operative pain (p = .001). Discussion: The results of the rapid review suggest a high to critical risk of bias in the studies included. The predisposing factors for post-operative pain differed widely across studies, but mainly included psychological factors as factors for post-operative pain. Pain management strategies should include an individualized approach and be implemented before, during and after the operation. For the descriptive-comparative study, there are substantial difficulties in discerning the effect of pain history or experience on post-operative pain using physiological or subjective reporting for conscious individuals due to risk of bias and using a unidimensional approach. Conclusion: Predisposing factors for post-operative pain should be screened in the pre-operative phase if possible, focusing on addressable factors whereas management of pain care strategies should include careful screening of participants biopsychosocial profile for elective surgery. The descriptive-comparative study suggests a possible, yet minimal benefit for managing patients’ expectation of pain related to the upcoming gastrointestinal surgery. The amount of pain medication in the recovery room is a significant predictor of maximal post-operative pain. Future research should include a larger sample, more variables related to pain and continue with a follow-up. Keywords: gastrointestinal, post-operative, pain, analgesia, anesthesiaTavoitteet: Katsauksen tarkoituksena oli tiivistää ja koota yhteen tutkijoille ja kliinikoille tietoa laparoskooppisten potilaiden postoperatiiviselle kivulle altistavista tekijöistä ja vallitsevista postoperatiivisista hoitokeinoista. Kuvailevan-vertailevan tutkimuksen tarkoituksena oli tutkia aiempien kipukokemusten ja lääkityksen välisiä yhteyksiä postoperatiivisen kivun voimakkuuteen potilailla, joille tehdään elektiivinen ruoansulatuskanavan leikkaus, käyttäen tietoja, jotka on kerätty esineiden internetiin perustuvalla älykkäällä kivunarviointityökalulla. Menetelmät: Katsausta varten tehtiin hakuja PubMed-, Web of Science- ja Embase-tietokannoista. ROBINS-I-työkalua käytettiin satunnaistamattomien tutkimusten laadun arviointiin, kun taas satunnaistettujen kontrolloitujen tutkimusten osalta käytettiin ROB 2-työkalua. Kuvailevaan-vertailevaan tutkimukseen otettiin mukaan 50 potilasta Turun yliopistollisessa sairaalassa tehtyjen ruoansulatuskanavan leikkausten jälkeen. Tutkimuksen aineistonkeruun suoritti Turun yliopiston henkilökuntaan kuuluva tutkija Turun yliopistollisessa sairaalassa. Aineiston analysoinnissa käytettiin kuvailevia ja vertailevia analyysimenetelmiä. Kuvailevia tilastoja käytettiin osallistujien tulosten, diagnoosien, toimenpiteiden ja kivun kokemiseen liittyvien muuttujien perusteella tehtyjen ryhmittelyjen esittämiseen ja analysointiin (esim. maksimaalisen kiputason graafinen mittaaminen numeerisella arviointiasteikolla). Vertailevia tilastoja käytettiin yhdistelmiin ja korrelaatioihin, jotka koskivat aiempia kiputiloja, lääkkeitä, pelkoa ja kivun odotusta maksimaalisen kiputason suhteen ruoansulatuskanavan leikkausten jälkeen Turun yliopistollisessa sairaalassa. Tulokset: Katsauksen tulokset viittaavat siihen, että potilaan psykologinen profiili vaikuttaa moniin leikkauksen jälkeiselle kivulle altistaviin tekijöihin. Näihin tekijöihin kuuluvat ahdistus, pelko, masennus, kivun odotus ja muut ruoansulatuskanavan leikkaukseen liittyvät tekijät. Tämän katsauksen tuloksissa kuvataan kuitenkin myös akuutti preoperatiivinen kipu, kirurgiset tekijät, genetiikka, ikä, sukupuoli, lihavuus ja aiemmat kokemukset kivusta merkityksellisinä altistavina tekijöinä ruoansulatuskanavan leikkauksen jälkeiselle kivulle. Ruoansulatuskanavan leikkauksen jälkeisiin kivunhoitostrategioihin kuuluu farmakologisten ja ei-farmakologisten toimenpiteiden käyttö. Kirjallisuuden mukaan ei-farmakologisia toimenpiteitä käytetään liian vähän, ja niitä olisi edistettävä farmakologisten kivunhoitostrategioiden lisänä elektiivisen ruoansulatuskanavan leikkauksen jälkeen. Kuvailevan ja vertailevan tutkimuksen tulokset ovat jossain määrin ristiriidassa nopean katsauksen tulosten kanssa. Aiemmat kipukokemukset tai aiempien kivuliaiden tapahtumien muistaminen eivät olleet yhteydessä ylimääräisen kipulääkityksen antamiseen leikkauksen jälkeen (p = 0,741). Leikkauksen jälkeiseen tulevaan kipuun liittyvä pelko ei ollut yhteydessä leikkauksen invasiivisuuteen (p = 0,662). Lisäksi kivun odotuksen (p = 0,698), tulevaan kirurgiseen toimenpiteeseen liittyvän kivun pelon (p = 0,637) ja leikkauksen jälkeisen lääkityksen (p = 0,481) välinen yhteys maksimaalisen leikkauksen jälkeisen kivun voimakkuuteen todettiin merkityksettömäksi. Tämän tutkimuksen tulokset viittaavat siihen, että potilaan odotukset ovat mahdollinen interventioalue, jolla voidaan parantaa leikkauksen jälkeistä kiputilannetta. Kipulääkityksen antaminen heräämössä ja kipulääkityksen määrä heräämössä olivat merkittäviä postoperatiivisen maksimaalisen kivun ennustajia (p = .001). Pohdinta: Katsauksen tulokset viittaavat siihen, että mukana olleissa tutkimuksissa on suuri tai kriittinen harhan riski. Postoperatiiviselle kivulle altistavat tekijät vaihtelivat suuresti eri tutkimuksissa, mutta niihin sisältyi pääasiassa psykologisia tekijöitä postoperatiivisen kivun tekijöinä. Kivunhoitostrategioihin olisi sisällyttävä yksilöllinen lähestymistapa, ja niitä olisi sovellettava ennen leikkausta, sen aikana ja sen jälkeen. Kuvailevassa ja vertailevassa tutkimuksessa on huomattavia vaikeuksia havaita kipuhistorian tai -kokemuksen vaikutusta leikkauksen jälkeiseen kipuun fysiologisen tai subjektiivisen raportoinnin avulla tietoisten yksilöiden osalta, koska on olemassa harhan riski ja koska käytetään yksiulotteista lähestymistapaa. Johtopäätökset: Kivunhoitostrategioihin olisi kuuluttava osallistujien biopsykososiaalisen profiilin huolellinen seulonta valintaleikkausta varten. Kuvaileva-vertaileva tutkimus viittaa siihen, että potilaiden tulevaan ruoansulatuskanavan leikkaukseen liittyvien kipuodotusten hallinnasta on mahdollista, joskin vähäistä hyötyä. Kipulääkkeiden määrä heräämössä on merkittävä leikkauksen jälkeisen maksimaalisen kivun ennustaja. Tulevaan tutkimukseen olisi sisällytettävä suurempi otos, enemmän kipuun liittyviä muuttujia ja jatkettava seurantaa
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