930 research outputs found

    Personality Dysfunction Manifest in Words : Understanding Personality Pathology Using Computational Language Analysis

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    Personality disorders (PDs) are some of the most prevalent and high-risk mental health conditions, and yet remain poorly understood. Today, the development of new technologies means that there are advanced tools that can be used to improve our understanding and treatment of PD. One promising tool – indeed, the focus of this thesis – is computational language analysis. By looking at patterns in how people with personality pathology use words, it is possible to gain access into their constellation of thinking, feelings, and behaviours. To date, however, there has been little research at the intersection of verbal behaviour and personality pathology. Accordingly, the central goal of this thesis is to demonstrate how PD can be better understood through the analysis of natural language. This thesis presents three research articles, comprising four empirical studies, that each leverage computational language analysis to better understand personality pathology. Each paper focuses on a distinct core feature of PD, while incorporating language analysis methods: Paper 1 (Study 1) focuses on interpersonal dysfunction; Paper 2 (Studies 2 and 3) focuses on emotion dysregulation; and Paper 3 (Study 4) focuses on behavioural dysregulation (i.e., engagement in suicidality and deliberate self-harm). Findings from this research have generated better understanding of fundamental features of PD, including insight into characterising dimensions of social dysfunction (Paper 1), maladaptive emotion processes that may contribute to emotion dysregulation (Paper 2), and psychosocial dynamics relating to suicidality and deliberate self-harm (Paper 3) in PD. Such theoretical knowledge subsequently has important implications for clinical practice, particularly regarding the potential to inform psychological therapy. More broadly, this research highlights how language can provide implicit and unobtrusive insight into the personality and psychological processes that underlie personality pathology at a large-scale, using an individualised, naturalistic approach

    Combined Nutrition and Exercise Interventions in Community Groups

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    Diet and physical activity are two key modifiable lifestyle factors that influence health across the lifespan (prevention and management of chronic diseases and reduction of the risk of premature death through several biological mechanisms). Community-based interventions contribute to public health, as they have the potential to reach high population-level impact, through the focus on groups that share a common culture or identity in their natural living environment. While the health benefits of a balanced diet and regular physical activity are commonly studied separately, interventions that combine these two lifestyle factors have the potential to induce greater benefits in community groups rather than strategies focusing only on one or the other. Thus, this Special Issue entitled “Combined Nutrition and Exercise Interventions in Community Groups” is comprised of manuscripts that highlight this combined approach (balanced diet and regular physical activity) in community settings. The contributors to this Special Issue are well-recognized professionals in complementary fields such as education, public health, nutrition, and exercise. This Special Issue highlights the latest research regarding combined nutrition and exercise interventions among different community groups and includes research articles developed through five continents (Africa, Asia, America, Europe and Oceania), as well as reviews and systematic reviews

    Continuous Estimation of Smoking Lapse Risk from Noisy Wrist Sensor Data Using Sparse and Positive-Only Labels

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    Estimating the imminent risk of adverse health behaviors provides opportunities for developing effective behavioral intervention mechanisms to prevent the occurrence of the target behavior. One of the key goals is to find opportune moments for intervention by passively detecting the rising risk of an imminent adverse behavior. Significant progress in mobile health research and the ability to continuously sense internal and external states of individual health and behavior has paved the way for detecting diverse risk factors from mobile sensor data. The next frontier in this research is to account for the combined effects of these risk factors to produce a composite risk score of adverse behaviors using wearable sensors convenient for daily use. Developing a machine learning-based model for assessing the risk of smoking lapse in the natural environment faces significant outstanding challenges requiring the development of novel and unique methodologies for each of them. The first challenge is coming up with an accurate representation of noisy and incomplete sensor data to encode the present and historical influence of behavioral cues, mental states, and the interactions of individuals with their ever-changing environment. The next noteworthy challenge is the absence of confirmed negative labels of low-risk states and adequate precise annotations of high-risk states. Finally, the model should work on convenient wearable devices to facilitate widespread adoption in research and practice. In this dissertation, we develop methods that account for the multi-faceted nature of smoking lapse behavior to train and evaluate a machine learning model capable of estimating composite risk scores in the natural environment. We first develop mRisk, which combines the effects of various mHealth biomarkers such as stress, physical activity, and location history in producing the risk of smoking lapse using sequential deep neural networks. We propose an event-based encoding of sensor data to reduce the effect of noises and then present an approach to efficiently model the historical influence of recent and past sensor-derived contexts on the likelihood of smoking lapse. To circumvent the lack of confirmed negative labels (i.e., annotated low-risk moments) and only a few positive labels (i.e., sensor-based detection of smoking lapse corroborated by self-reports), we propose a new loss function to accurately optimize the models. We build the mRisk models using biomarker (stress, physical activity) streams derived from chest-worn sensors. Adapting the models to work with less invasive and more convenient wrist-based sensors requires adapting the biomarker detection models to work with wrist-worn sensor data. To that end, we develop robust stress and activity inference methodologies from noisy wrist-sensor data. We first propose CQP, which quantifies wrist-sensor collected PPG data quality. Next, we show that integrating CQP within the inference pipeline improves accuracy-yield trade-offs associated with stress detection from wrist-worn PPG sensors in the natural environment. mRisk also requires sensor-based precise detection of smoking events and confirmation through self-reports to extract positive labels. Hence, we develop rSmoke, an orientation-invariant smoking detection model that is robust to the variations in sensor data resulting from orientation switches in the field. We train the proposed mRisk risk estimation models using the wrist-based inferences of lapse risk factors. To evaluate the utility of the risk models, we simulate the delivery of intelligent smoking interventions to at-risk participants as informed by the composite risk scores. Our results demonstrate the envisaged impact of machine learning-based models operating on wrist-worn wearable sensor data to output continuous smoking lapse risk scores. The novel methodologies we propose throughout this dissertation help instigate a new frontier in smoking research that can potentially improve the smoking abstinence rate in participants willing to quit

    Improving diagnostic procedures for epilepsy through automated recording and analysis of patients’ history

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    Transient loss of consciousness (TLOC) is a time-limited state of profound cognitive impairment characterised by amnesia, abnormal motor control, loss of responsiveness, a short duration and complete recovery. Most instances of TLOC are caused by one of three health conditions: epilepsy, functional (dissociative) seizures (FDS), or syncope. There is often a delay before the correct diagnosis is made and 10-20% of individuals initially receive an incorrect diagnosis. Clinical decision tools based on the endorsement of TLOC symptom lists have been limited to distinguishing between two causes of TLOC. The Initial Paroxysmal Event Profile (iPEP) has shown promise but was demonstrated to have greater accuracy in distinguishing between syncope and epilepsy or FDS than between epilepsy and FDS. The objective of this thesis was to investigate whether interactional, linguistic, and communicative differences in how people with epilepsy and people with FDS describe their experiences of TLOC can improve the predictive performance of the iPEP. An online web application was designed that collected information about TLOC symptoms and medical history from patients and witnesses using a binary questionnaire and verbal interaction with a virtual agent. We explored potential methods of automatically detecting these communicative differences, whether the differences were present during an interaction with a VA, to what extent these automatically detectable communicative differences improve the performance of the iPEP, and the acceptability of the application from the perspective of patients and witnesses. The two feature sets that were applied to previous doctor-patient interactions, features designed to measure formulation effort or detect semantic differences between the two groups, were able to predict the diagnosis with an accuracy of 71% and 81%, respectively. Individuals with epilepsy or FDS provided descriptions of TLOC to the VA that were qualitatively like those observed in previous research. Both feature sets were effective predictors of the diagnosis when applied to the web application recordings (85.7% and 85.7%). Overall, the accuracy of machine learning models trained for the threeway classification between epilepsy, FDS, and syncope using the iPEP responses from patients that were collected through the web application was worse than the performance observed in previous research (65.8% vs 78.3%), but the performance was increased by the inclusion of features extracted from the spoken descriptions on TLOC (85.5%). Finally, most participants who provided feedback reported that the online application was acceptable. These findings suggest that it is feasible to differentiate between people with epilepsy and people with FDS using an automated analysis of spoken seizure descriptions. Furthermore, incorporating these features into a clinical decision tool for TLOC can improve the predictive performance by improving the differential diagnosis between these two health conditions. Future research should use the feedback to improve the design of the application and increase perceived acceptability of the approach

    Digital agriculture: research, development and innovation in production chains.

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    Digital transformation in the field towards sustainable and smart agriculture. Digital agriculture: definitions and technologies. Agroenvironmental modeling and the digital transformation of agriculture. Geotechnologies in digital agriculture. Scientific computing in agriculture. Computer vision applied to agriculture. Technologies developed in precision agriculture. Information engineering: contributions to digital agriculture. DIPN: a dictionary of the internal proteins nanoenvironments and their potential for transformation into agricultural assets. Applications of bioinformatics in agriculture. Genomics applied to climate change: biotechnology for digital agriculture. Innovation ecosystem in agriculture: Embrapa?s evolution and contributions. The law related to the digitization of agriculture. Innovating communication in the age of digital agriculture. Driving forces for Brazilian agriculture in the next decade: implications for digital agriculture. Challenges, trends and opportunities in digital agriculture in Brazil

    Artificial Intelligence for Cognitive Health Assessment: State-of-the-Art, Open Challenges and Future Directions

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    The subjectivity and inaccuracy of in-clinic Cognitive Health Assessments (CHA) have led many researchers to explore ways to automate the process to make it more objective and to facilitate the needs of the healthcare industry. Artificial Intelligence (AI) and machine learning (ML) have emerged as the most promising approaches to automate the CHA process. In this paper, we explore the background of CHA and delve into the extensive research recently undertaken in this domain to provide a comprehensive survey of the state-of-the-art. In particular, a careful selection of significant works published in the literature is reviewed to elaborate a range of enabling technologies and AI/ML techniques used for CHA, including conventional supervised and unsupervised machine learning, deep learning, reinforcement learning, natural language processing, and image processing techniques. Furthermore, we provide an overview of various means of data acquisition and the benchmark datasets. Finally, we discuss open issues and challenges in using AI and ML for CHA along with some possible solutions. In summary, this paper presents CHA tools, lists various data acquisition methods for CHA, provides technological advancements, presents the usage of AI for CHA, and open issues, challenges in the CHA domain. We hope this first-of-its-kind survey paper will significantly contribute to identifying research gaps in the complex and rapidly evolving interdisciplinary mental health field

    Outcome Measurement in Functional Neurological Symptom Disorder

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    Outcome measurement in Functional Neurological Symptom Disorders (FNSDs) is particularly complex. Pressing questions include what kind of measure is more accurate or meaningful, or how to achieve standardisation in a clinically heterogenous group where subjective and objective observations of the same construct may deviate. This project aimed to build on the limited knowledge of measuring outcomes in FNSDs and attempts to address one of its inherent complexities; where clinical aspects of the disorder confound the usual prioritisation of "objective" over "subjective" (or patient-rated) measures. This PhD comprised a literature review and three research studies, each using different measures to assess the current status and (potential) outcomes in FNSD patients. A narrative description of systematically identified literature on stress, distress, and arousal measures in FNSD presents an overarching profile of the relationships between subjective and objective study measures. Eighteen studies (12 functional seizures, six other FNSD) capturing 396 FNSD patients were included. Eleven reported no correlation between subjective and objective measures. Only four studies reported significant correlations (r's=-0.74-0.59, p's <0.05). The small number of studies and diverse methodologies limit the conclusions of this review. However, the review's findings underscore the importance of validating outcome measures in patients with FNSD, carefully selecting the most appropriate measures for the research objectives, and possibly combining different measures optimally to triangulate a patient's current state, level of functioning or disability. Study One used factor analysis and Rasch modelling to investigate the psychometric properties of a novel FNSD-specific resource-based measure developed as an outcome measure for psychological therapies (The sElf-efficacy, assertiveness, Social support, self-awareness and helpful thinking (EASE) questionnaire). A 4-factor model identified self-efficacy (SE), self-awareness/assertiveness (SA), social support (SS) and interpersonal illness burden (IIB) as relevant domains. Each latent scale fits the Rasch model after refinement of the category responses and removing two items. With further improvement, the EASE-F has the potential to reliably measure self-reported SE, SA, SS, and IIB constructs which were found to be meaningful to patients with FNSD. This can identify patients with strengths and deficits in these constructs, allowing therapists to individualise interventions. Recommendations for refinement of future instrument versions, using the measure in clinical practice, and research in FNSDs are discussed. Study Two sought to understand the urgent and emergency care (UEC) service usage patterns among FNSD patients. Retrospective FNSD patient data from 2013 to 2016 UEC records (including NHS 111 calls, ambulance services, A&E visits, and acute admissions) were used to compare FNSD UEC usage rates with those of the general population and to model rates before and after psychotherapy. FNSD patients displayed 23 to 60 times higher UEC usage than the general population. Emergency service usage rates showed a significant reduction in level (rate level change = -0.90--0.70, p's <0.05) immediately after psychotherapy. While this study was uncontrolled, and a causal relationship between psychotherapy and reduced UEC service use cannot be proven by its design, the decrease in pre-treatment service usage among FNSD patients mirrors treatment-related improvements in health status and functioning previously documented using self-reported outcome measures. Further research is warranted to elucidate features of emergency care service use by patients with FNSD, assess interventions' cost-effectiveness, and help to optimise limited health care resource allocation. Study Three utilised a delay discounting and emotional bias task to assess if these measures could indicate the health state of FNSD patients and to compare findings in patients with those in healthy controls. This online-based study collected data on cognitive-affective functioning, decision-making and, indirectly, emotion regulation, alongside self-reported health data and indicators of mood while completing the tasks. Delay discounting (DD) was steeper in patients with FNSD, indicating a preference for less subjectively valuable immediate rewards. Patients displayed priming and interference effects for angry and happy facial expressions, which differed from the interference effects observed in healthy controls [F(1,76) = 3.5, p = 0.037, η2p = 0.084]. Modest associations (r's =0.26-0.33, p's <0.05) were found between the DD estimates and self-reported generalised anxiety, but not current feelings of anxiety in FNSD. There were no correlations with indices for negative affective priming or interference. These measures did not show predictive ability for self-reported difficulty regulating emotions, anxiety, depression or coping in FNSD. However, the fact that the DD task and self-reported constructs failed to correlate does not invalidate this objective test. The findings underscore the importance of using a combined approach to outcome measurement. This project highlights the importance of a more comprehensive understanding of outcomes and measures that capture clinically valid and meaningful health information. Given that subjective and objective measures capture different aspects of health state or function, a combination of measurement approaches will likely produce the most comprehensive understanding of patients' current state or treatment outcome. Because of the attentional, emotional, and perceptual alterations implicated in FNSD and the variable external representations of these, the difference between objective and subjective measures represents an interesting observation in its own right. The size of the discrepancy between subjective and objective measures may provide additional valuable insights into the underlying pathology. Nonetheless, there is still a need for standardisation and consistency in FNSD outcome measurement and reporting. Several important factors, such as the timeframe of measures, the influence of confounding factors, and the variety of presentation of any aspect of the disorder (e.g., physiological, cognitive, social, or behavioural presentations of arousal/stress), will need to be considered when designing and interpreting measurements for research or clinical analysis of the patient group

    Water and Brain Function: Effects of Hydration Status on Neurostimulation and Neurorecording

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    Introduction: TMS and EEG are used to study normal neurophysiology, diagnose, and treat clinical neuropsychiatric conditions, but can produce variable results or fail. Both techniques depend on electrical volume conduction, and thus brain volumes. Hydration status can affect brain volumes and functions (including cognition), but effects on these techniques are unknown. We aimed to characterize the effects of hydration on TMS, EEG, and cognitive tasks. Methods: EEG and EMG were recorded during single-pulse TMS, paired-pulse TMS, and cognitive tasks from 32 human participants on dehydrated (12-hour fast/thirst) and rehydrated (1 Liter oral water ingestion in 1 hour) testing days. Hydration status was confirmed with urinalysis. MEP, ERP, and network analyses were performed to examine responses at the muscle, brain, and higher-order functioning. Results: Rehydration decreased motor threshold (increased excitability) and shifted the motor hotspot. Significant effects on TMS measures occurred despite being re-localized and re-dosed to these new parameters. Rehydration increased SICF of the MEP, magnitudes of specific TEP peaks in inhibitory protocols, specific ERP peak magnitudes and reaction time during the cognitive task. Rehydration amplified nodal inhibition around the stimulation site in inhibitory paired-pulse networks and strengthened nodes outside the stimulation site in excitatory and CSP networks. Cognitive performance was not improved by rehydration, although similar performance was achieved with generally weaker network activity. Discussion: Results highlight differences between mild dehydration and rehydration. The rehydrated brain was easier to stimulate with TMS and produced larger responses to external and internal stimuli. This is explainable by the known physiology of body water dynamics, which encompass macroscopic and microscopic volume changes. Rehydration can shift 3D cortical positioning, decrease scalp cortex distance (bringing cortex closer to stimulator/recording electrodes), and cause astrocyte swelling-induced glutamate release. Conclusions: Previously unaccounted variables like osmolarity, astrocyte and brain volumes likely affect neurostimulation/neurorecording. Controlling for and carefully manipulating hydration may reduce variability and improve therapeutic outcomes of neurostimulation. Dehydration is common and produces less excitable circuits. Rehydration should offer a mechanism to macroscopically bring target cortical areas closer to an externally applied neurostimulation device to recruit greater volumes of tissue and microscopically favor excitability in the stimulated circuits
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