Big Five personality facets explaining variance in anxiety and depressive symptoms in a community sample

BACKGROUND

Personality traits are risk and protective factors in affective disorders. However, few studies have investigated the role of narrow personality facets, with existing research yielding contradictory results. Previous research has mostly focused on simple correlations. Several studies have performed separate multiple regressions within each trait, and have used non-standard measures of personality, making it difficult to determine how individual facets make unique contributions.

METHOD

This study performed secondary analysis of the NewMood data set (collected 2004-2009), comprising 264 participants from Greater Manchester. Participants provided self-reports of all NEO-PI-R personality facets, and semi-structured questionnaires of clinical depression and anxiety. All personality facets were entered into multiple regressions to explain variance in depression and anxiety.

RESULTS

Variance in both anxiety and depression were explained by a small number of personality facets, namely facet depression (referring to demotivation), facets positive emotion and assertiveness in extroversion, and facet competence in conscientiousness.

LIMITATIONS

This study relies on cross-sectional data and cannot determine causation. This study uses a mostly female sample, and the results were not stratified by sex due to the small sample.

CONCLUSION

Previous studies suggest that broad trait neuroticism positively associates with affective disorders; this study adds that the effect of neuroticism is limited to facet depression (related to demotivation). Contrary to previous studies, no facet of agreeableness or openness explained variance in affective disorders, and facet assertiveness positively associated with affective disorder scores. These findings may help to improve treatment matching and explain the mechanisms through which affective disorders develop.

Using the NANA toolkit at home to predict older adults' future depression

BACKGROUND

Depression is currently underdiagnosed among older adults. As part of the Novel Assessment of Nutrition and Aging (NANA) validation study, 40 older adults self-reported their mood using a touchscreen computer over three, one-week periods. Here, we demonstrate the potential of these data to predict future depression status.

METHODS

We analysed data from the NANA validation study using a machine learning approach. We applied the least absolute shrinkage and selection operator with a logistic model to averages of six measures of mood, with depression status according to the Geriatric Depression Scale 10 weeks later as the outcome variable. We tested multiple values of the selection parameter in order to produce a model with low deviance. We used a cross-validation framework to avoid overspecialisation, and receiver operating characteristic (ROC) curve analysis to determine the quality of the fitted model.

RESULTS

The model we report contained coefficients for two variables: sadness and tiredness, as well as a constant. The cross-validated area under the ROC curve for this model was 0.88 (CI: 0.69-0.97).

LIMITATIONS

While results are based on a small sample, the methodology for the selection of variables appears suitable for the problem at hand, suggesting promise for a wider study and ultimate deployment with older adults at increased risk of depression.

CONCLUSIONS

We have identified self-reported scales of sadness and tiredness as sensitive measures which have the potential to predict future depression status in older adults, partially addressing the problem of underdiagnosis.

Validation of the NANA (Novel Assessment of Nutrition and Ageing) touch screen system for use at home by older adults

Prospective measurement of nutrition, cognition, and physical activity in later life would facilitate early detection of detrimental change and early intervention but is hard to achieve in community settings. Technology can simplify the task and facilitate daily data collection. The Novel Assessment of Nutrition and Ageing (NANA) toolkit was developed to provide a holistic picture of an individual's function including diet, cognition and activity levels. This study aimed to validate the NANA toolkit for data collection in the community. Forty participants aged 65 years and over trialled the NANA toolkit in their homes for three 7-day periods at four-week intervals. Data collected using the NANA toolkit were compared with standard measures of diet (four-day food diary), cognitive ability (processing speed) and physical activity (self-report). Bland-Altman analysis of dietary intake (energy, carbohydrates, protein fat) found a good relationship with the food diary and cognitive processing speed and physical activity (hours) were significantly correlated with their standard counterparts. The NANA toolkit enables daily reporting of data that would otherwise be collected sporadically while reducing demands on participants; older adults can complete the daily reporting at home without a researcher being present; and it enables prospective investigation of several domains at once.

Cognitive visual perceptual deficits in patients with delirium

BACKGROUND

Delirium is a neuropsychiatric disorder characterised by severe cognitive impairment, but the specific neuropsychological profile of this condition has not yet been clearly delineated. Psychiatric symptoms of perceptual disturbance--such as hallucinations, illusions and misperceptions--are common in delirium, suggesting that patients may have deficits in the cognitive systems underlying visual perception.

METHODS

Five neuropsychological tests of visual perception were administered to 17 older patients with delirium, as well as to two control groups comprising 14 patients with Alzheimer's dementia and 18 cognitively normal patients. The Mini Mental State Examination and the Consortium to Establish a Registry for Alzheimer's Disease verbal memory test were also administered to assess the specificity of any perceptual impairments.

RESULTS

Patients with delirium scored significantly lower than cognitively normal patients on all perceptual tasks and significantly lower than patients with dementia on three of these tasks. Mini Mental State Examination scores did not differ between the delirium and dementia groups, and patients with delirium showed significantly better verbal recognition performance than those with dementia.

CONCLUSIONS

Patients with delirium have specific visual perceptual deficits that cannot be accounted for by general cognitive impairment. These novel findings provide insights into the neural mechanisms underling delirium and might help to improve clinical detection and management of the disorder. The results also support previous suggestions that cognitive perceptual deficits play a causal role in eliciting psychiatric symptoms of perceptual disturbance.