Prevalence and correlates of metabolic syndrome in Hong Kong Chinese adults—a random community sample study

The study investigates the prevalence and correlates of metabolic syndrome (MS) among Hong Kong Chinese adults. Random cluster sampling design and International Diabetes Federation (IDF) MS criteria were adopted. Totally 737 adults participated in questionnaire survey and received waist circumference (WC) measurement. Among them 335 showed central obesity (WC > 80 and 85 cm for women and men respectively). Subsequently 229 accepted blood test. Totally 71 participants met MS criteria, suggesting an overall prevalence of 14.1%. Both central obesity and MS increase sharply with age. Chi-square/ANOVA analyses revealed significant positive associations between central obesity with: being male, marital status being married/cohabit, lower education level, occupation as managers or housewives, and alcohol consumption. For MS, the significant positive correlates were: lower education level, occupation as service workers or housewives, lower income level, and alcohol consumption. After controlling for age, binary logistic regression analyses suggested the significant risk factors for central obesity were being male (OR = 1.4), married/cohabit (OR = 1.8), longer working hours (OR = 1.5), eating less vegetables (OR = 1.5), and alcohol consumption (OR = 1.8). For MS, only alcohol consumption appeared to be a significant risk factor (OR = 2.3). Multivariate binary logistic regressions also supported that age group and alcohol consumption were significant predictors of MS. To conclude, adopting randomized cluster sampling and IDF criteria, the study revealed a prevalence rate of MS at 14.1%. Alcohol consumption appears to be the strongest risk factor of MS, which however needs further investigation.     

What kind of kind is intelligence?

The model of human intelligence that is most widely adopted derives from psychometrics and behavioral genetics. This standard approach conceives intelligence as a general cognitive ability that is genetically highly heritable and describable using quantitative traits analysis. The paper analyzes intelligence within the debate on natural kinds and contends that the general intelligence conceptualization does not carve psychological nature at its joints. Moreover, I argue that this model assumes an essentialist perspective. As an alternative, I consider an HPC theory of intelligence and evaluate how it deals with essentialism and with intuitions coming from cognitive science. Finally, I highlight some concerns about the HPC model as well, and conclude by suggesting that it is unnecessary to treat intelligence as a kind in any sense.     

Detecting Clusters/Communities in Social Networks

Cohen's κ, a similarity measure for categorical data, has since been applied to problems in the data mining field such as cluster analysis and network link prediction. In this paper, a new application is examined: community detection in networks. A new algorithm is proposed that uses Cohen's κ as a similarity measure for each pair of nodes; subsequently, the κ values are then clustered to detect the communities. This paper defines and tests this method on a variety of simulated and real networks. The results are compared with those from eight other community detection algorithms. Results show this new algorithm is consistently among the top performers in classifying data points both on simulated and real networks. Additionally, this is one of the broadest comparative simulations for comparing community detection algorithms to date.     

A general proof of consistency of heuristic classification for cognitive diagnosis models

The Asymptotic Classification Theory of Cognitive Diagnosis (Chiu et al., 2009, Psychometrika, 74, 633–665) determined the conditions that cognitive diagnosis models must satisfy so that the correct assignment of examinees to proficiency classes is guaranteed when non‐parametric classification methods are used. These conditions have only been proven for the Deterministic Input Noisy Output AND gate model. For other cognitive diagnosis models, no theoretical legitimization exists for using non‐parametric classification techniques for assigning examinees to proficiency classes. The specific statistical properties of different cognitive diagnosis models require tailored proofs of the conditions of the Asymptotic Classification Theory of Cognitive Diagnosis for each individual model – a tedious undertaking in light of the numerous models presented in the literature. In this paper a different way is presented to address this task. The unified mathematical framework of general cognitive diagnosis models is used as a theoretical basis for a general proof that under mild regularity conditions any cognitive diagnosis model is covered by the Asymptotic Classification Theory of Cognitive Diagnosis.     

Specificity of MMPI cluster types to chronic illness

Abstract

Cluster analysis derived MMPI profile types were compared for three distinct illness populations: chronic low back pain (CLBP), mixed headache (HA), and cardiac disease (CD). Results replicated previous findings in the chronic pain and cardiac literatures. Differences between diagnostic groups and genders were found for the prevalence of the derived MMPI profile types. Results indicated that, in males, the two chronic pain groups had similar profile types and similar relative frequencies of these types, but both differed from the CD sample. The chronic pain groups tended to have a greater number of patients with pathological and distressed MMPI profile types and a lesser representation in the subclinical profile type compared to the CD sample. Similar findings were obtained for females, though the CLBP group tended to have a higher frequency of highly distressed, pathological profiles than either the HA or CD groups. Results suggested that the MMPI may be a measure of response to illness rather than reflecting predisposing personality types for any given illness and that the higher frequency of highly distressed profiles in the chronic pain samples reflects the increased suffering of those populations compared to other illnesses.     

A Typology of Anger Profiles in Couples

ABSTRACT

Few researchers have attempted to identify couples' anger management profiles. Cluster analysis was conducted using five indices of anger derived from Inventory of Anger Experience in Couples (Laughrea, Belanger, & Wright, 1996). Sample consisted of 220 couples from normal and clinical populations. Four types were identified: (a) Relaxed Couples, (b) Slightly Angry Couples, (c) Explosive Angry Couples, and (d) Dangerously Enraged Couples. Results also indicated that the higher the disturbance of anger dynamics between partners, the more psychologically and maritally distressed they were, and the lower the quality of their coping strategies. Understanding anger and its clinical implication are discussed.     

Changes in Catastrophizing and Depressed Mood During and After Early Cognitive Behaviorally Oriented Interventions for Pain

Catastrophizing and depressed mood are risk factors for poor outcome in treatments for pain and appear to act as mediators for favorable outcome. However, little is known about how catastrophizing and depressed mood co-occur within individuals and how these patterns change during treatment, which is the focus of the current study. The study uses data from a randomized controlled trial about early cognitive behaviorally oriented interventions for patients with nonspecific spinal pain (N = 84). Cluster analyses were used to extract subgroups of individuals with similar scoring patterns on catastrophizing and depressed mood at pretreatment, mid-treatment, posttreatment, and at 6 months' follow-up. To track individual progress, the clusters were linked over time. The analyses revealed four clusters: “low depression and catastrophizing”, “high depression and catastrophizing”, “high depression”, and “high catastrophizing”. There was little individual transition from one scoring pattern to another across time, not at least for those scoring high on both depressed mood and catastrophizing. Moreover, high stability within this cluster was related to low levels of psychological flexibility at baseline. It is concluded that catastrophizing and depressed mood at the start of treatment were likely to remain high despite a cognitive behavioral intervention and that a lack of psychological flexibility may have a role.     

Presurgical MMPI-2 Cluster Profiles Predict 1-Year Low-Back Surgery Outcomes

The primary purpose of this study was to examine whether empirically derived cluster profiles based on scores from the Minnesota Multiphasic Personality Inventory-2 (MMPI-2) predicted outcomes of surgery at 1 year for patients with low-back pain. On the basis of hierarchical cluster analysis of presurgery MMPI-2 scores for 60 patients, three interpretable and significant subgroups were formed. These consisted of a pathological/neurotic type, a double V-code type, and a normal type. The patents in the normal type were significantly more likely to report beneficial outcomes of surgery in terms of disability and pain than those in the other 2 types. These findings extend previous research and suggest (1) low-back surgery candidates are heterogeneous in their psychological profiles, and (2) MMPI-2 profiles are predictive of low-back surgery outcome. Future research should focus on identifying physiological substrates for these distinct profiles and investigating the effectiveness of presurgical psychological interventions based on profile type.