Physical activity, leisure-time screen use and depression among children and young adolescents

Objectives:
Adolescent mental disorders remain a relatively neglected area of research, despite evidence that these conditions affect youth disproportionately. We examined associations between physical activity, leisure-time screen use and depressive symptoms among Australian children and adolescents.

Design:
Large cross-sectional observational study.

Methods:
Self-reported physical activity and leisure-time screen behaviours, and depressive symptoms using the Short Mood and Feeling Questionnaire were assessed in 8256 students aged 10–16 years (mean age = 11.5 years, SD = 0.8).

Results:
Thirty three percent of the sample reported moderate to high depressive symptoms, with rates higher among females (OR = 1.18; 95% CI: 1.02, 1.36; p = 0.001). Increased opportunities to be active at school outside class (OR = 0.70; 0.58, 0.85; p < 0.001), being active in physical education classes (OR = 0.77; 0.69, 0.86; p < 0.001), greater involvement in sports teams at school (OR = 0.77; 0.67, 0.88; p < 0.001) and outside of school (OR = 0.84; 0.73, 0.96; p = 0.01) were all independently associated with lower odds for depressive symptoms. Meeting recommended guidelines for physical activity (OR = 0.62; 0.44, 0.88; p = 0.007) and, for 12–14 year olds, leisure-time screen use (OR = 0.77; 0.59, 0.99; p = 0.04) were also independently associated with lower odds for depressive symptoms.

Conclusions:
Higher levels of physical activity among children and young adolescents, and lower levels of leisure-time screen use among young adolescents, are associated with lower depressive symptoms. Longitudinal studies are needed to understand the causal relationships between these variables.

Altered cardiac autonomic nervous function in depression

Background:
Depression is an independent risk factor for coronary artery disease. Autonomic instability may play a mediating or moderating role in this relationship; however this is not well understood. The objective of this study was to explore cardiac autonomic function and cardiac arrhythmia in depression, the correlation between depression severity and Heart Rate Variability (HRV) related indices, and the prevalence of arrhythmia.

Methods:
Individuals (n = 53) with major depression as assessed by the Diagnostic and Statistical Manual of Mental Disorders, who had a Hamilton Rating Scale for Depression (HAMD) score ≥20 and a Zung Self-Rating Depression Scale score > 53 were compared to 53 healthy individuals, matched for age and gender. Multichannel Electrocardiograph ECG-92C data were collected over 24 hours. Long-term changes in HRV were used to assess the following vagally mediated changes in autonomic tone, expressed as time domain indices: Standard deviation of the NN intervals (SDNN), standard deviation of 5 min averaged NN intervals (SDANN), Root Mean Square of the Successive Differences (RMSSD) and percentage of NN intervals > 50 ms different from preceding interval (pNN50). Pearson’s correlations were conducted to explore the strength of the association between depression severity (using the SDS and HRV related indices, specifically SDNN and low frequency domain / high frequency domain (LF/HF)).

Results:
The values of SDNN, SDANN, RMSSD, PNN50 and HF were lower in the depression group compared to the control group (P<.05). The mean value of the LF in the depression group was higher than the in control group (P<.05). Furthermore the ratio of LF/HF was higher among the depression group than the control group (P<.05). A linear relationship was shown to exist between the severity of the depression and HRV indices. In the depression group, the prevalence of arrhythmia was significantly higher than in the control group (P<.05), particularly supraventricular arrhythmias.

Conclusions:
Our findings suggest that depression is accompanied by dysfunction of the cardiac autonomic nervous system, and further, that depression severity is linked to severity of this dysfunction. Individuals with depression appear to be susceptible to premature atrial and/or ventricular disease.

Biological phenotypes underpin the physio-somatic symptoms of somatization, depression, and chronic fatigue syndrome

Objective
Somatization is a symptom cluster characterized by ‘psychosomatic’ symptoms, that is, medically unexplained symptoms, and is a common component of other conditions, including depression and myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). This article reviews the data regarding the pathophysiological foundations of ‘psychosomatic’ symptoms and the implications that this has for conceptualization of what may more appropriately be termed physio-somatic symptoms.

Method
This narrative review used papers published in PubMed, Scopus, and Google Scholar electronic databases using the keywords: depression and chronic fatigue, depression and somatization, somatization and chronic fatigue syndrome, each combined with inflammation, inflammatory, tryptophan, and cell-mediated immune (CMI).

Results
The physio-somatic symptoms of depression, ME/CFS, and somatization are associated with specific biomarkers of inflammation and CMI activation, which are correlated with, and causally linked to, changes in the tryptophan catabolite (TRYCAT) pathway. Oxidative and nitrosative stress induces damage that increases neoepitopes and autoimmunity that contribute to the immuno-inflammatory processes. These pathways are all known to cause physio-somatic symptoms, including fatigue, malaise, autonomic symptoms, hyperalgesia, intestinal hypermotility, peripheral neuropathy, etc.

Conclusion
Biological underpinnings, such as immune-inflammatory pathways, may explain, at least in part, the occurrence of physio-somatic symptoms in depression, somatization, or myalgic encephalomyelitis/chronic fatigue syndrome and thus the clinical overlap among these disorders.

Cognitive-reminiscence therapy and usual care for depression in young adults: study protocol for a randomized controlled trial

Background
Depression is a common affliction for young adults, and is associated with a range of adverse outcomes. Cognitive-reminiscence therapy is a brief, structured intervention that has been shown to be highly effective for reducing depressive symptoms, yet to date has not been evaluated in young adult populations. Given its basis in theory-guided reminiscence-based therapy, and incorporation of effective therapeutic techniques drawn from cognitive therapy and problem-solving frameworks, it is hypothesized to be effective in treating depression in this age group.

Methods and design
This article presents the design of a randomized controlled trial implemented in a community-based youth mental health service to compare cognitive-reminiscence therapy with usual care for the treatment of depressive symptoms in young adults. Participants in the cognitive-reminiscence group will receive six sessions of weekly, individual psychotherapy, whilst participants in the usual-care group will receive support from the youth mental health service according to usual procedures. A between-within repeated-measures design will be used to evaluate changes in self-reported outcome measures of depressive symptoms, psychological wellbeing and anxiety across baseline, three weeks into the intervention, post-intervention, one month post-intervention and three months post-intervention. Interviews will also be conducted with participants from the cognitive-reminiscence group to collect information about their experience receiving the intervention, and the process underlying any changes that occur.

Discussion
This study will determine whether a therapeutic approach to depression that has been shown to be effective in older adult populations is also effective for young adults. The expected outcome of this study is the validation of a brief, evidence-based, manualized treatment for young adults with depressive symptoms.

Reminiscence-based therapies for depression: should they be used only with older adults?

Reminiscence-based therapies have been reliably evidenced to be an effective intervention for depression. However, to date, their use has been restricted primarily to older adults. This article reviews empirical findings related to the various functions of reminiscence and their correlates with mental health. Reminiscence-based interventions and their effectiveness are then reviewed, with a particular focus on recent evaluations of structured reminiscence-based therapies that utilize preexisting therapeutic frameworks for the treatment for depression. The exclusive use of reminiscence-based therapies with older adult populations is then challenged, and it is argued that these approaches may be useful for reducing depression symptomatology for young and middle-aged adults also. Considerations for the use of reminiscence-based therapies in these populations are discussed, and future directions for research are presented.

Job strain — attributable depression in a sample of working Australians: assessing the contribution to health inequalities

Background The broad aim of this study was to assess the contribution of job strain to mental health inequalities by (a) estimating the proportion of depression attributable to job strain (low control and high demand jobs), (b) assessing variation in attributable risk by occupational skill level, and (c) comparing numbers of job strain–attributable depression cases to numbers of compensated 'mental stress' claims. Methods Standard population attributable risk (PAR) methods were used to estimate the proportion of depression attributable to job strain. An adjusted Odds Ratio (OR) of 1.82 for job strain in relation to depression was obtained from a recently published meta-analysis and combined with exposure prevalence data from the Australian state of Victoria. Job strain exposure prevalence was determined from a 2003 population-based telephone survey of working Victorians (n = 1101, 66% response rate) using validated measures of job control (9 items, Cronbach's alpha = 0.80) and psychological demands (3 items, Cronbach's alpha = 0.66). Estimates of absolute numbers of prevalent cases of depression and successful stress-related workers' compensation claims were obtained from publicly available Australian government sources. Results Overall job strain-population attributable risk (PAR) for depression was 13.2% for males [95% CI 1.1, 28.1] and 17.2% [95% CI 1.5, 34.9] for females. There was a clear gradient of increasing PAR with decreasing occupational skill level. Estimation of job strain–attributable cases (21,437) versus "mental stress" compensation claims (696) suggest that claims statistics underestimate job strain–attributable depression by roughly 30-fold. Conclusion Job strain and associated depression risks represent a substantial, preventable, and inequitably distributed public health problem. The social patterning of job strain-attributable depression parallels the social patterning of mental illness, suggesting that job strain is an important contributor to mental health inequalities. The numbers of compensated 'mental stress' claims compared to job strain-attributable depression cases suggest that there is substantial under-recognition and under-compensation of job strain-attributable depression. Primary, secondary, and tertiary intervention efforts should be substantially expanded, with intervention priorities based on hazard and associated health outcome data as an essential complement to claims statistics.

An empirical comparison of classification algorithms for diagnosis of depression from brain sMRI scans

To be diagnostically effective, structural magnetic resonance imaging (sMRI) must reliably distinguish a depressed individual from a healthy individual at individual scans level. One of the tasks in the automated diagnosis of depression from brain sMRI is the classification. It determines the class to which a sample belongs (i.e., depressed/not depressed, remitted/not-remitted depression) based on the values of its features. Thus far, very limited works have been reported for identification of a suitable classification algorithm for depression detection. In this paper, different types of classification algorithms are compared for effective diagnosis of depression. Ten independent classification schemas are applied and a comparative study is carried out. The algorithms are: Naïve Bayes, Support Vector Machines (SVM) with Radial Basis Function (RBF), SVM Sigmoid, J48, Random Forest, Random Tree, Voting Feature Intervals (VFI), LogitBoost, Simple KMeans Classification Via Clustering (KMeans) and Classification Via Clustering Expectation Minimization (EM) respectively. The performances of the algorithms are determined through a set of experiments on sMRI brain scans. An experimental procedure is developed to measure the performance of the tested algorithms. A classification accuracy evaluation method was employed for evaluation and comparison of the performance of the examined classifiers.