Comparison of cognitive functioning as measured by the Ruff Figural Fluency Test and the CogState computerized battery within the LifeLines Cohort Study

Contains fulltext : 174524.pdf (publisher's version ) (Open Access)BACKGROUND: The Ruff Figural Fluency Test (RFFT; a pencil and paper test) and the CogState (a computerized cognitive test battery) are well-validated and suitable tests to evaluate cognitive functioning in large observational studies at the population level. The LifeLines Cohort Study includes the RFFT as baseline measurement and incorporated the CogState as replacement for the RFFT at follow-up. It is unknown how these two tests relate to each other. Therefore, the aim of this study is to examine the correlation between the RFFT and the CogState and the impact of demographic characteristics on this association. METHODS: A subcohort of the LifeLines Cohort Study, a large population based cohort study, participated in this study. Correlations between the RFFT and six subtasks of the CogState were examined. Subgroup analyses were performed to investigate the influence of age, education, and gender on the results. With sensitivity analyses we investigated the influence of computer experience and (physical) impairments. RESULTS: A total of 509 participants (mean age (SD): 53 years (14.6); range 18-87 years) participated in this study. All correlations between the RFFT and the CogState were statistically significant (except for the correlation between the RFFT error ratio and the CogState One Back Task), ranging from -0.39 to 0.28. Stratifying the analyses for age, education, and gender did not substantially affect our conclusions. Sensitivity analyses showed no substantial influence of level of computer experience or (physical) impairments. CONCLUSIONS: Correlations found in the present study were only weak to moderate, indicating that cognitive functioning measured by the RFFT does not measure the same components of cognitive functioning as six subtasks of the CogState. Computerized testing such as the CogState may be very well suited for large cohort studies to assess cognitive functioning in the general population and to identify cognitive changes as early as possible, as it is a less time- and labor intensive tool

Seeing the signs: Using the course of residual depressive symptomatology to predict patterns of relapse and recurrence of major depressive disorder

BACKGROUND: Major depressive disorder (MDD) is characterized by high relapse/recurrence rates. Predicting individual patients' relapse/recurrence risk has proven hard, possibly due to course heterogeneity among patients. This study aimed to (1) identify homogeneous data-driven subgroups with different patterns of relapse/recurrence and (2) identify associated predictors. METHODS: For a year, we collected weekly depressive symptom ratings in 213 primary care MDD patients. Latent class growth analyses (LCGA), based on symptom-severity during the 24 weeks after no longer fulfilling criteria for the initial major depressive episode (MDE), were used to identify groups with different patterns of relapse/recurrence. Associations of baseline predictors with these groups were investigated, as were the groups' associations with 3- and 11-year follow-up depression outcomes. RESULTS: LCGA showed that heterogeneity in relapse/recurrence after no longer fulfilling criteria for the initial MDE was best described by four classes: "quick symptom decline" (14.0%), "slow symptom decline" (23.3%), "steady residual symptoms" (38.7%), and "high residual symptoms" (24.1%). The latter two classes showed lower self-esteem at baseline, and more recurrences and higher severity at 3-year follow-up than the first two classes. Moreover, the high residual symptom class scored higher on neuroticism and lower on extraversion and self-esteem at baseline. Interestingly, the steady residual symptoms and high residual symptoms classes still showed higher severity of depressive symptoms after 11 years. CONCLUSION: Some measures were associated with specific patterns of relapse/recurrence. Moreover, the data-driven relapse/recurrence groups were predictive of long-term outcomes, suggesting that patterns of residual symptoms could be of prognostic value in clinical practice

Childhood Abuse and Increased Risk for Anger in Depression during Adulthood: The Moderating Role of MAOA Genotype and Gender

Stress-related psychiatric disorders across the life spa