The relationship between physical activity and mental health has been widely investigated, and several hypotheses have been formulated about it. Specifically, during the aging process, physical exercise might represent a potential adjunctive treatment for neuropsychiatric disorders and cognitive impairment, helping delay the onset of neurodegenerative processes. Even though exercise itself might act as a stressor, it has been demonstrated that it reduces the harmful effects of other stressors when performed at moderate intensities. Neurotransmitter release, neurotrophic factor and neurogenesis, and cerebral blood flow alteration are some of the concepts involved. In this review, the potential effects of exercise on the aging process and on mental health are discussed, concerning some of the recent findings on animal and human research. The overwhelming evidence present in the literature today suggests that exercise ensures successful brain functioning.

Objective: The aim of this meta-analysis is to evaluate the effect of aerobic training and strength training as a treatment for depression in patients diagnosed with major depressive disorder. Methods: PubMed (Medline), ISI knowledge (Institute for Scientific Information), SciELO (Scientific Electronic Library) and Scopus databases were consulted from January 1970 to September 2011. Data were collected on variables as follows: total number of patients (pre- and postintervention), age, randomized (yes or no), diagnostic criteria, assessment instruments, and the percentage of remission and treatment response. Subsequently, we collected information on time intervention, intensity, duration, frequency, method of training (aerobic training and strength training) and type of supervision. Standardized mean differences were used for pooling continuous variables as endpoint scores. Binary outcomes, such as proportion of remission (no symptoms) and at least 50% reduction of initial scores (response), were pooled using relative risks. Random effects models were used that take into account the variance within and between studies. Results: Ten articles were selected and subdivided by their interventions, controlled training modality and levels of intensity. As there was no statistically significant difference between the two types of intervention (strength or aerobic training), we combined data which finally showed a 0.61 (95% CI: –0.88 to –0.33) standard deviation reduction in the intervention group compared to the control group. When the analysis was restricted only to those studies that used the Hamilton scale (n = 15), we observed a reduction of 3.49 points compared with the control group. Conclusion: Despite the heterogeneity of the studies, the present meta-analysis concluded that physical exercise improves the response to treatment, especially aerobic training. However, the efficacy of exercise in the treatment of depression was influenced by age and severity of symptoms.

Aim: To study quality of life among the elderly with dementia in institutions. Methods: Patients above 60 years with dementia, 82 in nursing home and 74 in departments of geriatric psychiatry, were included. They were assessed with the Quality of Life in Late-Stage Dementia (QUALID); the Self-Maintenance scale, Mini Mental State Examination (MMSE) and Clinical Dementia Rating scale. Patient’s age, gender, previous medical and psychiatric history were recorded. Dementia was diagnosed according to ICD-10 criteria for research. Based on information in an interview with the patient and a carer and information in the patient’s record, a geriatric psychiatrist made a diagnosis of major depression according to DSM-IV, if present. Results: The patients’ mean (± SD) age was 82.9 ± 7.7 years, 103 (66%) were women. A factor analysis of the QUALID scale resulted in two factors: ‘discomfort’ and ‘comfort’. Three linear regression analyses were performed. Variables associated with lower quality of life (total QUALID score) were: a diagnosis of major depression (p < 0.001), lower score on MMSE (p = 0.032), impaired function in activities of daily living (p = 0.007) and female gender (p = 0.046). Variables associated with the ‘discomfort’ subscale score were: major depression (p < 0.001), lower score on MMSE (p = 0.006) and living in a department of geriatric psychiatry (p = 0.041). The ‘comfort’ subscale score was associated with impaired function in activities of daily living (p < 0.001). Explained variance for the three models was 34, 33 and 23%, respectively. Conclusion: Quality of life is diminished among elderly patients in institutions and the most marked correlates were a diagnosis of major depression, worse performance in activities of daily living and worse cognitive function.

Population aging has been occurring as a global phenomenon with heterogeneous consequences in both developed and developing countries. Neurodegenerative diseases, such as Alzheimer׳s Disease (AD), have high prevalence in the elderly population. Early diagnosis of this type of disease allows early treatment and improves patient quality of life. This paper proposes a Bayesian network decision model for supporting diagnosis of dementia, AD and Mild Cognitive Impairment (MCI). Bayesian networks are well-suited for representing uncertainty and causality, which are both present in clinical domains. The proposed Bayesian network was modeled using a combination of expert knowledge and data-oriented modeling. The network structure was built based on current diagnostic criteria and input from physicians who are experts in this domain. The network parameters were estimated using a supervised learning algorithm from a dataset of real clinical cases. The dataset contains data from patients and normal controls from the Duke University Medical Center (Washington, USA) and the Center for Alzheimer׳s Disease and Related Disorders (at the Institute of Psychiatry of the Federal University of Rio de Janeiro, Brazil). The dataset attributes consist of predisposal factors, neuropsychological test results, patient demographic data, symptoms and signs. The decision model was evaluated using quantitative methods and a sensitivity analysis. In conclusion, the proposed Bayesian network showed better results for diagnosis of dementia, AD and MCI when compared to most of the other well-known classifiers. Moreover, it provides additional useful information to physicians, such as the contribution of certain factors to diagnosis.

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The presence of caregiver depression and patient delusions should always be part of the multidisciplinary evaluation of dementia cases.

Unawareness of memory impairment in PwD varies across international regions. Our data support the notion that unawareness should be seen not only as a common neurobiological feature of dementia, increasing with severity of dementia, but also as a phenomenon influenced by social and cultural factors.

Recent evidence suggests that Alzheimer's disease (AD) and depression share common mechanisms of pathogenesis. In particular, deregulation of glutamate-mediated excitatory signaling may play a role in brain dysfunction in both AD and depression. We have investigated levels of glutamate and its precursor glutamine in the cerebrospinal fluid (CSF) of patients with a diagnosis of probable AD or major depression compared to healthy controls and patients with hydrocephalus. Patients with probable AD or major depression showed significantly increased CSF levels of glutamate and glutamine compared to healthy controls or hydrocephalus patients. Furthermore, CSF glutamate and glutamine levels were inversely correlated to the amyloid tau index, a biomarker for AD. Results suggest that glutamate and glutamine should be further explored as potential CSF biomarkers for AD and depression.