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Exercise is an effective treatment for positive valence symptoms in major depression.

ABSTRACT: INTRODUCTION:Measurement of symptoms domains and their response to treatment in relative isolation from diagnosed mental disorders has gained new urgency, as reflected by the National Institute of Mental Health's introduction of the Research Domain Criteria (RDoC). The Snaith Hamilton Pleasure Scale (SHAPS) and the Motivation and Energy Inventory (MEI) are two scales measuring positive valence symptoms. We evaluated the effect of exercise on positive valence symptoms of Major Depressive Disorder (MDD). METHODS:Subjects in the Treatment with Exercise Augmentation for Depression (TREAD) study completed self-reported SHAPS and MEI during 12 weeks of exercise augmentation for depression. We evaluated the effect of exercise on SHAPS and MEI scores, and whether the changes were related to overall MDD severity measured with the Quick Inventory of Depression Symptomatology (QIDS). RESULTS:SHAPS and MEI scores significantly improved with exercise. MEI score change had larger effect size and greater correlation with change in QIDS score. MEI also showed significant moderator and mediator effects of exercise in MDD. LIMITATIONS:Generalizability to other treatments is limited. This study lacked other bio-behavioral markers that would enhance understanding of the relationship of RDoC and the measures used. CONCLUSIONS:Positive valence symptoms improve with exercise treatment for depression, and this change correlates well with overall outcome. Motivation and energy may be more clinically relevant to outcome of exercise treatment than anhedonia.

SUBMITTER: Toups M

PROVIDER: S-EPMC6036912 | biostudies-literature | 2017 Feb

REPOSITORIES: biostudies-literature

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Exercise is an effective treatment for positive valence symptoms in major depression.

Toups Marisa M Carmody Thomas T Greer Tracy T Rethorst Chad C Grannemann Bruce B Trivedi Madhukar H MH

Journal of affective disorders 20161015

<h4>Introduction</h4>Measurement of symptoms domains and their response to treatment in relative isolation from diagnosed mental disorders has gained new urgency, as reflected by the National Institute of Mental Health's introduction of the Research Domain Criteria (RDoC). The Snaith Hamilton Pleasure Scale (SHAPS) and the Motivation and Energy Inventory (MEI) are two scales measuring positive valence symptoms. We evaluated the effect of exercise on positive valence symptoms of Major Depressive ...[more]

PMID: 27936452

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Project description:BackgroundHeterogeneity in major depressive disorder (MDD) is well recognized but not well understood. Core depressive features are reward and emotional symptoms, which reflect dysfunctions in the positive valence (PV) and negative valence (NV) systems, respectively. This study assessed whether PV and NV systems (based on selected symptoms) were associated with different clinical features, antidepressant response, and levels of immunomarkers in adults with MDD.MethodsThese analyses used data from combining medications to enhance depression outcomes study (N = 665; n = 166 for immunomarkers). PV and NV symptom scores were extracted from the clinician-rated 30-item Inventory of Depressive Symptomatology. Correlational analyses were conducted.ResultsPV and NV symptom scores were substantially associated with different clinical features. PV symptoms (impaired motivation, impaired energy, and anhedonia) were independently associated with female gender (p < .001), older age (p = .012), and higher cognitive and physical impairment (p < .001) according to the 7-item Cognitive and Physical Functioning Questionnaire. Conversely, NV symptoms (anxiety and interpersonal sensitivity) were independently associated with younger age (p = .013), more anxious comorbidities (p = .001 for generalized anxiety disorder and p = .002 for social phobia) and other commonly associated noncriterion symptoms (p < .001). Overall, PV symptoms were more responsive to antidepressants than NV symptoms (p < .0001; Cohen's d = .455). A PV symptom score was positively correlated with the concentration of three proinflammatory and one anti-inflammatory factor. In contrast, an NV symptom score was negatively associated with only one proinflammatory immunomarker.ConclusionsPV and NV system functions appear to be reflected in selected clinical symptoms that differentially relate to other clinical features, treatment outcomes, and immunological function.

Project description:AimTo examine the contribution of treatment resistant depression (TRD) to mortality in depressed post-myocardial infarction (MI) patients independent of biological and social predictors.MethodsThis secondary analysis study utilizes the Enhancing Recovery in Coronary Heart Disease (ENRICHD) clinical trial data. From 1834 depressed patients in the ENRICHD study, there were 770 depressed post-MI patients who were treated for depression. In this study, TRD is defined as having a less than 50% reduction in Hamilton Depression (HAM-D) score from baseline and a HAM-D score of greater than 10 in 6 mo after depression treatment began. Cox regression analysis was used to examine the independent contributions of TRD to mortality after controlling for the biological and social predictors.ResultsTRD occurred in 13.4% (n = 103) of the 770 patients treated for depression. Patients with TRD were significantly younger in age (P = 0.04) (mean = 57.0 years, SD = 11.7) than those without TRD (mean = 59.2 years, SD = 12.0). There was a significantly higher percentage of females with TRD (57.3%) compared to females without TRD (47.4%) [χ(2) (1) = 4.65, P = 0.031]. There were significantly more current smokers with TRD (44.7%) than without TRD (33.0%) [χ(2) (1) = 7.34, P = 0.007]. There were no significant differences in diabetes (P = 0.120), history of heart failure (P = 0.258), prior MI (P = 0.524), and prior stroke (P = 0.180) between patients with TRD and those without TRD. Mortality was 13% (n = 13) in patients with TRD and 7% (n = 49) in patients without TRD, with a mean follow-up of 29 mo (18 mo minimum and maximum of 4.5 years). TRD was a significant independent predictor of mortality (HR = 1.995; 95%CI: 1.011-3.938, P = 0.046) after controlling for age (HR = 1.036; 95%CI: 1.011-1.061, P = 0.004), diabetes (HR = 2.912; 95%CI: 1.638-5.180, P < 0.001), heart failure (HR = 2.736; 95%CI: 1.551-4.827, P = 0.001), and smoking (HR = 0.502; 95%CI: 0.228-1.105, P = 0.087).ConclusionThe analysis of TRD in the ENRICHD study shows that the effective treatment of depression reduced mortality in depressed post-MI patients. It is important to monitor the effectiveness of depression treatment and change treatments if necessary to reduce depression and improve cardiac outcomes in depressed post-MI patients.

Project description:Background and objectiveEfficiently capturing the severity of positive valence symptoms could aid in risk stratification for adverse outcomes among patients with psychiatric disorders and identify optimal treatment strategies for patient subgroups. Motivated by the success of convolutional neural networks (CNNs) in classification tasks, we studied the application of various CNN architectures and their performance in predicting the severity of positive valence symptoms in patients with psychiatric disorders based on initial psychiatric evaluation records.MethodsPsychiatric evaluation records contain unstructured text and semi-structured data such as question-answer pairs. For a given record, we tokenise and normalise the semi-structured content. Pre-processed tokenised words are represented as one-hot encoded word vectors. We then apply different configurations of convolutional and max pooling layers to automatically learn important features from various word representations. We conducted a series of experiments to explore the effect of different CNN architectures on the classification of psychiatric records.ResultsOur best CNN model achieved a mean absolute error (MAE) of 0.539 and a normalized MAE of 0.785 on the test dataset, which is comparable to the other well-known text classification algorithms studied in this work. Our results also suggest that the normalisation step has a great impact on the performance of the developed models.ConclusionsWe demonstrate that normalisation of the semi-structured contents can improve the MAE among all CNN configurations. Without advanced feature engineering, CNN-based approaches can provide a comparable solution for classifying positive valence symptom severity in initial psychiatric evaluation records. Although word embedding is well known for its ability to capture relatively low-dimensional similarity between words, our experimental results show that pre-trained embeddings do not improve the classification performance. This phenomenon may be due to the inability of word embeddings to capture problem specific contextual semantic information implying the quality of the employing embedding is critical for obtaining an accurate CNN model.

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Exercise is an effective treatment for positive valence symptoms in major depression.

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Exercise is an effective treatment for positive valence symptoms in major depression.

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