55 research outputs found
Predictors of Cyberchondria during the COVID-19 pandemic: A cross-sectional study using supervised machine learning.
No full textBACKGROUND
Cyberchondria is characterized by repeated and compulsive online searches for health information, resulting in increased health anxiety and distress. It has been conceptualized as a multi-dimensional construct fueled by both anxiety and compulsivity-related factors and described as a "transdiagnostic compulsive behavioral syndrome" which is associated with health anxiety, problematic internet use and obsessive-compulsive symptoms. Cyberchondria is not included in the ICD-11 or the DSM-5, and its defining features, etiological mechanisms and assessment continue to be debated.
OBJECTIVE
This study aimed to investigate changes in the severity of cyberchondria during the pandemic and identify predictors of cyberchondria at this time.
METHODS
Data collection started on May 4, 2020 and ended on June 10, 2020, which corresponds to the first wave of the COVID-19 pandemic in Europe. At the time the present study took place, French-speaking countries in Europe (France, Switzerland, Belgium and Luxembourg) all implemented lockdown or semi-lockdown measures. The survey consisted of a questionnaire collecting demographic information (sex, age, education level and country of residence) and information on socioeconomic circumstances during the first lockdown (e.g., economic situation, housing and employment status), and was followed by several instruments assessing various psychological and health-related constructs. Inclusion criteria for the study were being at least 18 years of age and having a good understanding of French. Self-report data were collected from 725 participants aged 18 to 77 years (mean 33.29, SD 12.88 years), with females constituting the majority (416/725, 57.4%).
RESULTS
The results show that the COVID-19 pandemic affected various facets of cyberchondria: cyberchondria-related distress and interference with functioning increased (distress z=-3.651, P<.001; compulsion z=-5.697, P<.001), whereas the reassurance facet of cyberchondria decreased (z=-6.680, P<.001). Also, COVID-19-related fears and health anxiety emerged as the strongest predictors of cyberchondria-related distress and interference with functioning during the pandemic.
CONCLUSIONS
These findings provide evidence about the impact of the COVID-19 pandemic on cyberchondria and identify factors that should be considered in efforts to prevent and manage cyberchondria at times of public health crises. Also, they are consistent with the theoretical model of cyberchondria during the COVID-19 pandemic proposed by Starcevic and his colleagues in 2020. In addition, the findings have implications for the conceptualization and future assessment of cyberchondria
A network approach to understanding obsessions and compulsions.
No full textBackground: Efforts to understand the constellation of symptoms in obsessive-compulsive disorder (OCD) have typically relied on models where latent variable(s) are assumed to underlie all symptoms. In contrast, a network approach does not assume that there are underlying latent variables and allows for the possibility that clusters of symptoms may mutually reinforce each other. We aimed to determine whether obsessions and compulsions formed a coherent and mutually reinforcing network of symptoms.
Method: 400 participants were recruited online and administered the Obsessive-Compulsive Inventory-Revised (OCI-R). A network analysis was computed using an Extended Bayesian Information Criterion estimator.
Results: There were five communities of symptoms: 1. A mixed contamination and checking community, 2. An ordering/arranging community, 3. A superstitious/counting/repeating community, 4. A mixed hoarding and checking community, and 5. An intrusive thoughts community. In the accuracy check, edges displayed wide confidence intervals, indicating that edges’ strength could not be interpreted. Additional analyses at the level of OCI-R subscales indicated that checking was significantly more central than other subscales in the network.
Conclusions: Obsessions and compulsions may be related in a mutually reinforcing way, thereby constituting OCD as a psychopathological entity. Prospective investigations are needed to ascertain the directionality of relationships in the network
Behavioral addictions and the associated mental health issues and psychopathology
No full textIntroductionBehavioral addictions are conceptually controversial and their relationship with mental health problems and psychopathology is poorly understood.ObjectivesTo review the relationships between personality traits, mental health issues and mental disorders on one hand and several behavioral addictions on the other. The latter include problematic Internet use, Internet gaming disorder, hypersexual disorder/compulsive sexual behavior disorder, compulsive buying and exercise addiction.MethodsLiterature review and conceptual synthesis.ResultsMental health issues, personality dimensions and mental disorders are commonly associated with behavioral addictions. Although some relatively specific associations were found (e.g., between Internet gaming disorder and attention deficit/hyperactivity disorder, between compulsive buying and pathological hoarding and between exercise addiction and eating disorders), the specificity of most associations was low. Most studies were cross-sectional and the direction of causality, if any, was uncertain. Therefore, it is unknown under what circumstances certain mental health issues predispose to the particular behavioral addiction or represent a primary problem and when they are a consequence of behavioral addictions. This review also underscores the importance of distinguishing between certain behavioral addictions and overlapping conditions, e.g., between compulsive buying and bipolar disorder (mania/hypomania).ConclusionsThese findings suggest that proper conceptualization of behavioral addictions as distinct conditions or a manifestation of an underlying psychopathology will have to await results of the prospective studies. In the meantime, there are implications for treatment in terms of the importance of identifying and addressing the underlying or associated mental health problems in individuals with behavioral addictions.Disclosure of interestThe author has not supplied his/her declaration of competing interest.</jats:sec
Cyberchondria and its Relationships with Related Constructs: a Network Analysis.
No full textCyberchondria denotes repeated online searches for health information that are associated with increasing levels of health anxiety. The aims of this study were to apply network analysis to investigate the extent to which cyberchondria is a distinct construct, ascertain which of the related constructs have the strongest relationships with cyberchondria and investigate whether some of the symptoms of cyberchondria are more central to the construct of cyberchondria. Questionnaires assessing the severity of cyberchondria, health anxiety, obsessive-compulsive disorder symptoms, intolerance of uncertainty, problematic Internet use, anxiety, depression and somatic symptoms were administered to 751 participants who searched for health information online during a previous 3-month period and were recruited from an online crowdsourcing platform. Network analyses were used to compute the networks, perform community detection tests and calculate centrality indices. Results suggest that cyberchondria is a relatively specific syndrome-like construct, distinct from all related constructs and consisting of interrelated symptoms. It has the strongest relationships with problematic Internet use and health anxiety. No symptom of cyberchondria emerged clearly as more central to the construct of cyberchondria. Future research should aim to deepen our understanding of cyberchondria and its links with psychopathology, especially its close relationship with problematic Internet use
Approaches for dealing with structural- and rounded zeros in data mining tasks
No full textZeros can cause many issues in data analysis and dealing with them requires specialized procedures. We differentiate between rounded zeros, structural zeros and missing values. Rounded zeros occur when the true value of a variable is hidden because of a detection limit in whatever mechanism was used to acquire the data. Structural zeros are values which are truly zero, often coming about due to a hidden mechanism separate from the one which generates values greater than 0. Missing values are values that are completely missing for unknown or known reasons. This thesis outlines various methods for dealing with different kinds of zeros in different contexts. Many of these methods are very specific in their ideal usecase. They are separated based on which kind of zero they are intended for and if they are better suited for compositional or for standard data.
For rounded zeros we impute the zeros with an estimated value below the detection limit. The author describes multiplicative replacement, a simple procedure that imputes values at a fixed fraction of the detection limit. As a more advanced technique, the author describes Kaplan Meier smoothing spline replacement, which interpolates a spline on a Kaplan Meier curve and uses the spline below the detection limit to impute values in a more natural distribution. Rounded zeros cannot be imputed with the same techniques that would be used for regular missing values, since there is more information available on the true value of a rounded zero than there would be for a regular missing value.
Structural zeros cannot be imputed since they are a true zero. Imputing them would falsify their values and produce a value where there should be none. Because of this, we apply modelling techniques that can work around structural zeros and incorporate them. For standard data, the zero inflated Poisson model is presented. This model utilizes a mixture of a logistic and a Poisson distribution to accurately model data with a large amount of structural zeros. While the Poisson distribution is only applicable to count data, the zero inflation concept can be applied to different kinds of distributions. For compositional data, the zero adjusted Dirichlet model is introduced. This model mixes Dirichlet distributions for every pattern of zeros found within the data. Non-algorithmic techniques to reduce the amount of structural zeros present are also shown. These techniques being amalgamation, which combines columns with structural zeros into more broad descriptors and classification, which changes columns into categorical values based on a structural zero being present or not.
Missing values are values that are completely missing for various known or unknown reasons. Different imputation techniques are introduced. For standard data, MissForest imputation is introduced, which utilizes a RandomForest regression to impute mixed type missing values. Another imputation technique shown utilizes both a genetic algorithm and a neural network to impute values based on the genetic algorithm minimizing the error of an autoencoder neural network. In the case of compositional data, knn imputation is presented, which utilizes the knn concept also found in knn clustering to impute the values based on the closest samples with a value available.
All of these methods are explained and demonstrated to give readers a guide to finding the suitable methods to use in different scenarios.
The thesis also provides a general guide on dealing with zeros in data, with decision flowcharts and more detailed descriptions for both compositional and standard data being presented. General tips on getting better results when zeros are involved are also given and explained. This general guide was then applied to a dataset to show it in action.Nullen können viele Probleme in Datenanalyse verursachen. Ihre Präsenz blockiert bei Kompositionalen Daten die wichtige Log-Transformation und führt bei Standarddaten zu schlecht angepassten Verteilungen. Mit ihnen umzugehen benötigt spezielle Verfahren. Wir unterscheiden zwischen gerundeten Nullen, strukturellen Nullen und fehlenden Werten. Gerundete Nullen zeigen sich in Daten wenn der echte Wert einer Variable versteckt ist weil der Mechanismus der die Werte ausgibt ein Detektionslimit aufweist. Strukturelle Nullen sind Nullen die wirklich Null sind, und keinen Wert maskieren. In vielen Fällen werden sie durch einen seperaten Prozess generiert, als Werte die keine Strukturelle Null sind. Fehlende Werte sind Werte die wegen bekannten oder unbekannten Gründen vollkommen fehlen. Diese Arbeit beschreibt verschiede Methoden um mit verschiedenen Arten von Nullen in verschiedenen Umständen umzugehen. Viele von diesen Methoden sind spezifisch für ihr Einsatzgebiet. Sie sind basierend darauf getrennt für welche Nullen sie gedacht sind und ob sie besser für Kompositionale oder Standarddaten gedacht sind.
Für gerundete Nullen setzen wir Werte in die Null ein, die unter dem Detektionslimit liegen. Der Author beschreibt multiplikatives Ersetzen, eine einfache Prozedur die Nullen durch einen fixen Teil des Detektionslimits ersetzt. Als fortgeschrittenere Technik wird “Kaplan Meier Smoothing Spline replacement” beschrieben, welche ein Spline auf einer Kaplan Meier Kurve interpoliert und die Werte unter dem Detektionslimit benutzt um die Werte in einer natürlicheren Verteilung einzusetzen. Gerundete Nullen können nicht mit den selben Methoden ersetzt werden wie fehlende Werte da bei gerundeten Nullen mehr Information zu Verfügung steht, nämlich das sie unter einem gewissen Limit liegen.
Strukturelle Nullen können nicht ersetzt werden da sie wirklich Null sind. Sie zu ersetzen würde die Daten verfälschen und einen Wert produzieren wo eigentlich keiner vorkommt. Deswegen benutzen wir Modellierungstechniken die um sie herum arbeiten und sie mit ins Modell einbeziehen. Für Standarddaten wird das “Zero Inflated Poisson Model” vorgestellt. Dieses Model benutzt eine Mixtur von einer logistischen und einer Poissonverteilung um Daten mit vielen Nullen genau darzustellen. Man kann die Poisson Verteilung zwar nur für Zähldaten benutzen, allerdings ist das Konzept der “Zero Inflation” auch für andere Verteilungen benutzbar. Für kompositionale Daten wird das logistische Mixturmodell vorgestellt. Diese Modell vermischt logistische Verteilungen für jedes Muster von Nullen in einem Datensatz. Nicht-algorithmische Techniken um strukturelle Nullen zu reduzieren werden auch gezeigt. Diese Techniken sind “Amalgamation”, wobei mehrere Spalten miteinander kombiniert werden um einen übersichtlicheren Wert darzustellen, und Klassifizierung, wobei eine Spalte in eine kategorische Variable geändert wird die anzeigt ob der Wert eine strukturelle Null oder nicht ist.
Als fehlender Wert werden Werte beschrieben die unter verschiedenen Umständen nicht im Datensatz präsent sind. Verschiedene Techniken, um diese zu ersetzen, werden vorgestellt. Für Standarddaten wird die MissForest Imputierung vorgestellt, welche eine RandomForest Regression benutzt um gemischte Daten einzusetzen, also Daten die sowohl kontinuierliche als auch kategorische Variablen enthalten. Eine Weitere Methode benutzt einen genetischen Algorithmus und ein Neuronales Netz um Werte einzusetzen. Dies wird erreicht in dem der Genetische Algorithmus die Eingabe in ein Autoenkodierer Netzwerk so optimiert das der Fehler minimiert ist. Für kompositionale Daten wird KNN Imputation vorgestellt, welche das KNN Konzept benutzt, das auch in KNN Clustering benutzt wird, um Variablen basierend auf den nächsten Einträgen zu imputieren.
All diese Methoden werden erklärt und demonstriert um dem Leser/der Leserin eine Anleitung zu geben um die richtige Methode für das richtige Umfeld zu finden.
Die Arbeit stellt ebenfalls eine generelle Anleitung bereit um mit Nullen umzugehen. Es werden Ablaufdiagramme und genaue Beschreibungen für Kompositionale- und Standarddaten vorgestellt. Generelle Hinweise um bessere Ergebnisse bei der Analyse mit Nullen zu erhalten werden auch gegeben und erklärt. Im anschließenden Teil wurde diese Anleitung beispielsweise eingesetzt
Testing the spectrum hypothesis of problematic online behaviors: A network analysis approach.
No full textThe validity of the constructs of problematic Internet or smartphone use and Internet or smartphone addiction has been extensively debated. The spectrum hypothesis posits that problematic online behaviors (POBs) may be conceptualized within a spectrum of related yet distinct entities. To date, the hypothesis has received preliminary support, and further robust empirical studies are still needed. The present study tested the spectrum hypothesis of POBs in an Australian community sample (n = 1,617) using a network analysis approach. Psychometrically validated self-report instruments were used to assess six types of POBs: problematic online gaming, cyberchondria, problematic cybersex, problematic online shopping, problematic use of social networking sites, and problematic online gambling. A tetrachoric correlation matrix was computed to explore relationships between online activities and a network analysis was used to analyze relationships between POBs. Correlations between online activities were positive and significant, but of small magnitude (0.051 ≤ r ≤ 0.236). The community detection analysis identified six distinct communities, corresponding to each POB, with strong relationships between items within each POB and weaker relationships between POBs. These findings provide further empirical support for the spectrum hypothesis, suggesting that POBs occur as distinct entities and with little overlap
Working towards an international consensus on criteria for assessing Internet Gaming Disorder: a critical commentary on Petry et al. (2014)
No full textThis commentary paper critically discusses the recent debate paper by Petry et al. (2014) that argued there was now an international consensus for assessing Internet Gaming Disorder (IGD). Our collective opinions vary considerably regarding many different aspects of online gaming. However, we contend that the paper by Petry and colleagues does not provide a true and representative international community of researchers in this area. This paper critically discusses and provides commentary on (i) the representativeness of the international group that wrote the 'consensus' paper, and (ii) each of the IGD criteria. The paper also includes a brief discussion on initiatives that could be taken to move the field towards consensus. It is hoped that this paper will foster debate in the IGD field and lead to improved theory, better methodologically designed studies, and more robust empirical evidence as regards problematic gaming and its psychosocial consequences and impact
Percutaneous coronary intervention of an anomalous right coronary artery originating from the left coronary artery
No full textRelationship between benzodiazepine prescription, aggressive behavior, and behavioral disinhibition: a retrospective study in a Swiss prison.
No full textBACKGROUND
Benzodiazepines are commonly prescribed in prisons amidst the controversies surrounding their potential role in causing behavioral disinhibition and aggressive behavior and their association with use and trafficking of illicit and addictive substances. The present study aimed to (1) ascertain the relationship between benzodiazepine prescription (including their dosage and duration of use) and aggressive behavior and behavioral disinhibition in prison and (2) investigate whether there was an association between benzodiazepine prescription, (including their dosage and duration of use) and using and trafficking illicit and addictive substances during imprisonment.
METHODS
Data were extracted from the electronic database of an "open" Swiss prison (n = 1206, 1379 measures) over a 5-year period (2010-2015). Measures included benzodiazepine prescription, duration of benzodiazepine use and mean dosage, and punishable behaviors (physical and verbal aggression, disinhibited but not directly aggressive behaviors, property damage or theft, substance-related offenses, and rule transgression). We assessed the relationship between benzodiazepine prescription and punishable behaviors after propensity score matching. Logistic regressions were also used to test the relationship of benzodiazepine use duration and dosage with punishable behaviors among participants who received benzodiazepines.
RESULTS
After propensity score matching, benzodiazepine prescription was not significantly associated with any punishable behavior. Among detained persons who took benzodiazepines, there was no significant association of dosage and duration of use with offenses involving illicit or addictive substance use or trafficking.
CONCLUSIONS
Our study did not empirically support the occurrence of increased aggressive or disinhibited behaviors or increased risk of substance abuse in detained persons who received benzodiazepines in prison. This suggests a need to reconsider restrictions in prescribing benzodiazepines in the prison setting
Double Interatrial Septum and Patent Foramen Ovale in Woman With Transient Ischemic Attack
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