Personality Disorders. Severe Psychopathology. Medical Conditions. Who Benefits by Self-Help and Why? Back Matter Pages Included in the coverage: Recommendations for books, web sites, organizations, support groups, hotlines, and audio-visual materials Depression, anxiety, eating disorders, addictions, and other conditions.
Guidelines for evaluating self-help and guided self-support materials. Strategies for integrating self-help with traditional modes of therapy. Assessment tools for determining client appropriateness for self-help.
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New directions in theories of self-help and self-change. Contraindications for self-help approaches. Headaches are one of the most common neurological problems presented in the NHS; more than 10 million people in the UK experience regular or frequent headaches and they account for around one in 25 of all consultations in general practice. They are painful and debilitating and are a cause of absence from work or school. The NICE guideline advises on the diagnosis and management of the most common headache types seen by healthcare professionals.
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Also by Larry E. Beutler
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Quality Standards. In the depressed group, loss of self-esteem played a central role in the network. Self-esteem has been previously described as an important predictor of change in bipolar depression, suggesting that decreases in self-esteem are tightly linked to increases in other depressive symptoms [ 35 ], as was also displayed in the current analyses.
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Finally, in the minimally impaired group the typically manic symptoms of increased speech and elevated mood are most consistently identified as central symptoms in the network. This finding is more difficult to interpret in light of previous literature. In this group it is likely that specific symptom patterns are more difficult to detect, since these patients simply display less severe manic and depressed symptomatology. However, one possible explanation for these specific symptoms patterns, might be that if these minimally impaired patients develop symptoms, this will more likely lead to the activation of the central hypomanic symptoms which are by definition associated with less functional impairment.
However, since the minimally impaired group reported significantly less severe symptoms on many items, any differences with the other two groups should be interpreted with caution and may be due to differences in prevalence of manic and depressed symptoms. Although there are several important differences in symptoms centrality across the groups, decrease in energy level seems to be a highly central symptom in all three course groups.
Thus, changes in energy levels are strongly associated with changes in other symptoms in all networks.
Larry E. Beutler
In the light of the recent changes in diagnostic criteria of BD in the DSM-5 this is an interesting finding. Because of the lack of empirical evidence, the validity of this additional criterion has been disputed [ 36 , 37 ]. The current findings do show that changes in energy levels might be highly central symptoms in BD, but no direct evidence for a central role of energy increase is found here, since the currently found central energy decrease only reflects the energy decrease as a symptom of depression, and not mania.
Moreover, these findings are also in line with previous studies using the network approach which challenged the current categorical approach of psychopathology by showing the interconnection between symptoms of different psychiatric disorders [ 16 , 41 ]. The fact that manic and depressed symptoms are interconnected, overlap and often occur simultaneously implicates that mania and depression are not at all total opposite and distinct mood states, but lie on the same spectrum and presumably overlap with regards to their structure and connectivity strengths of the symptom networks.
From such a network perspective one could hypothesize that stronger interconnection and overlap between manic and depressed symptoms especially in the cycling group explains a cycling pattern rather than a stable state of either deep depression or mania. The latter would have been expected in case of non-overlapping attractors.
In BD decreased sleep, psychomotor agitation and irritability are symptoms associated both with mania and depression [ 40 ] and therefore possibly connecting both ends of the mood spectrum. For instance, decreased sleep could lead to increased energy levels and hypo- manic mood states, however after some time energy levels might eventually drop leading to a more depressed state.
A transition from mania to depression might for instance be due to insomnia within a manic mood state due to feelings of grandeur.
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This overlapping insomnia symptom may activate depressive symptoms such as loss of energy, restlessness, and concentration problems, subsequently leading to a depressed state. However this interpretation is rather tentative and underlines the need for confirmation in other BD populations as well as longitudinal monitoring of symptoms and their temporal development.
This is the first attempt to explain differences in bipolar mood course by investigating symptom associations through a network approach. This could be a first step in explaining the variety of course patterns that are clinically displayed by BD patients by investigating how bipolar symptoms interact and reinforce each other.
Further, the current study is also novel to the extent that manic and depressed symptoms are not treated as separate constructs, but fitted into the same model. However, some important limitations should be taken into account. First, patients were divided into three groups based on their longitudinal course patterns. Although these three specific course groups are previously described in the literature and roughly reflect what is observed in clinical practice, one could argue for the existence of many more different course groups [ 4 ].
Second, course data were assessed through monthly ratings on the LCM which provides a global impression of the disease course, but lacks detailed information about more subtle mood changes and every minor episode. Third, in the current outpatient sample severe mood states especially manic symptoms were rare, which also led to the exclusion of some symptom items because of low variance. This implicates that the current findings can only be translated to BD patients with relatively mild symptom severity.
Fourth, we used the timepoint with most severe symptomatology to construct the symptom networks, which requires careful interpretation to prevent the trap of circular argumentation.
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As the minimally impaired group by definition had low symptom severity this may partly explain some of the differences, although the other two groups showed important distinctions in their networks, but not in symptom severity. Last, although a clinical sample of patients is relatively large, for the analyses of 14 different variables within three groups of on average 40 patients, sample sizes are small.
Due to low statistical power, the current results should be interpreted with caution and replication in larger samples is needed. The current approach might be a first step in investigating the mechanisms behind the development of different bipolar course patterns by looking at bipolar symptoms themselves. Futher, longitudinal studies are needed to show whether symptom networks have truly predictive and clinical value. Within these studies follow-up measurement should be very frequent to allow for the detection of directed relations between bipolar symptoms.
In the current study follow-up measurements for the QIDS and YMRS were 6 months apart, making studying of the development of symptoms over time impossible, hence the cross-sectional symptom networks. Novel approaches such as the Experience Sampling Method [ 43 , 44 ] might be highly suitable for the measurement of moment-to-moment development of symptoms and by that gain insight in the causal chain in which symptoms interact in BD.
Identifying these patterns in bipolar patients might have great clinical value in predicting future mood course and ultimately the prevention of new mood episodes. Connections between the nodes are Spearman's rank correlation coefficients green: positive correlation coefficient, red: negative correlation coefficient based on the average edge weights between symtpoms of all 5 separate timepoints. Conceived and designed the experiments: ATS. Browse Subject Areas? Click through the PLOS taxonomy to find articles in your field.