Dynamics of the human stress system in depression:A combined population- and person-based approach to assess long-term changes and daily life fluctuations

Depression is a stress-related disorder, with an often chronic course. Studies into the biology of depression have often focused on a major component of the stress system, the hypothalamic-pituitary-adrenal (HPA) axis, which increases the release of the hormone cortisol upon activation by stress. Studies into the amount of cortisol in depressed versus non-depressed samples show inconsistent results. A possible reason for this is that they did not account for the fact that the production of cortisol fluctuates over the day and that functioning of the HPA axis may change over time. Studies described in this thesis suggest that the cortisol stress response is increased in individuals with acute depressive problems, but that it is decreased in individuals with a longer history of depressive problems. In addition, they also suggest that the presence of a relationship between depression and increased cortisol levels at the group level does not imply that depressed individuals can be discriminated by their cortisol levels. Therefore, the use of cortisol as biomarker for depression is currently ruled out. In this thesis, it was also examined whether a possible antidepressant effect of physical activity on depressive symptoms is explained by changes in functioning of the HPA axis. Regular exercise appeared to decrease depressive symptoms as expected, but changes in the cortisol stress response did not seem to underlie this effect. In addition, it was found that daily physical activity leads to an increase in positive emotions in nearly everyone, while the effect on negative emotions differs between individuals

Uncovering complexity details in actigraphy patterns to differentiate the depressed from the non-depressed

While the negative association between physical activity and depression has been well established, it is unclear what precise characteristics of physical activity patterns explain this association. Complexity measures may identify previously unexplored aspects of objectively measured activity patterns, such as the extent to which individuals show repetitive periods of physical activity and the diversity in durations of such repetitive activity patterns. We compared the complexity levels of actigraphy data gathered over 4 weeks ([Formula: see text] data points each) for every individual, from non-depressed ([Formula: see text] ) and depressed ([Formula: see text] ) groups using recurrence plots. Significantly lower levels of complexity were detected in the actigraphy data from the depressed group as compared to non-depressed controls, both in terms of lower mean durations of periods of recurrent physical activity and less diversity in the duration of these periods. Further, diagnosis of depression was not significantly associated with mean activity levels or measures of circadian rhythm stability, and predicted depression status better than these

Personalizing cognitive behavioral therapy for cancer-related fatigue using ecological momentary assessments followed by automated individual time series analyses:A case report series

Introduction: A common approach to personalizing psychological interventions is the allocation of treatment modules to individual patients based on cut-off scores on questionnaires, which are mostly based on group studies. However, this way, intraindividual variation and temporal dynamics are not taken into account. Automated individual time series analyses are a possible solution, since these can identify the factors influencing the targeted symptom in a specific individual, and associated modules can be allocated accordingly. The aim of this study was to illustrate how automated individual time series analyses can be applied to personalize cognitive behavioral therapy for cancer-related fatigue in cancer survivors and how this procedure differs from allocating modules based on questionnaires.Methods: This study was a case report series (n = 3). Patients completed ecological momentary assessments at the start of therapy, and after three treatment modules (approximately 14 weeks). Assessments were analyzed with AutoVAR, an R package that automates the process of finding optimal vector autoregressive models. The results informed the treatment plan.Results: Three cases were described. From the ecological momentary assessments and automated time series analyses three individual treatment plans were constructed, in which the most important predictor for cancer-related fatigue was treated first. For two patients, this led to the treatment ending after the follow-up ecological momentary assessments. One patient continued treatment until six months, the standard treatment time in regular treatment. All three treatment plans differed from the treatment plans informed by questionnaire scores.Discussion: This study is one of the first to apply time series analyses in systematically personalizing psychological treatment. An important strength of this approach is that it can be used for every modular cognitive behavioral intervention where each treatment module addresses specific maintaining factors. Whether or not personalized CBT is more efficacious than standard, non-personalized CBT remains to be determined in controlled studies comparing it to usual care.</p

Redefining Therapeutic Outcomes of Depression Treatment

Responses to evidence-based interventions for depression are divergent: Some patients benefit more than others during treatment and some do not benefit at all or even deteriorate. Tailoring interventions to the individual may improve outcomes. However, such personalization of evidence-based treatment in depression requires investigation of individual outcomes and the individual trajectories towards these outcomes. This theoretical paper provides a critical reflection on individual outcomes of depression treatment. First, it is argued that outcomes should be broadened, from a focus on mainly depressive symptomatology to recovery in different domains. It is acknowledged that recovery from depression reflects a personal journey that differs from person to person. Second, outcome measures should be lengthened beyond the acute treatment phase, taking a lifetime perspective on depression. The challenge then is to discover which trajectories of what measures during what interventions result in personalized sustainable recovery and for whom. Routine outcome monitoring systems may be used to inform this quest towards assessment of personalized sustainable therapeutic outcomes. Adaptations to broaden and lengthen measurements in routine outcome monitoring systems are proposed to identify predictors of personalized sustainable recovery. Routine outcome monitoring systems may eventually be used to implement personalized treatments for depression that result in personalized sustainable recovery

Overnight affective dynamics and sleep characteristics as predictors of depression and its development

INTRODUCTION: Greater affective inertia during the day (higher carry-over effects of prior affect to the current moment) is associated with depression and its development. However, the role of overnight affective inertia (from evening to morning) in depression, and the role of sleep therein, has been scarcely studied. OBJECTIVES: We examined i) the difference in overnight inertia for positive (PA) and negative affect (NA) between individuals with past depression, current depression, and no depression; ii) how sleep duration and quality influence overnight affective inertia in these groups, and iii) whether overnight affective inertia predicts depression development. METHODS: We used data of 579 women from the East-Flanders Prospective Twin Survey. First, individuals with past (n=82), current (n=26), and no depression (n=471) at baseline were examined, and then individuals who did (n=58) and did not (n=319) develop depression at 12-months follow-up. Affect was assessed 10 times a day for 5 days. Sleep was assessed with sleep diaries. Affective inertia was operationalized as the influence of affect(t-1) on affect(t). Linear mixed-effect models were used to test the hypotheses. RESULTS: Overnight affective inertia was not associated with depression, neither was it differently associated with sleep characteristics in the depression groups. However, sleep characteristics were more negatively associated with morning NA in both depression groups compared to the non-depressed group. Overnight affective inertia did not predict the development of depression at follow-up. CONCLUSIONS: Depression and sleep characteristics might be more related to mean affect levels rather than to more complex emotion dynamics measures. Replication of these findings with longer time-series is needed

Single-Subject Research in Psychiatry:Facts and Fictions

Scientific evidence in the field of psychiatry is mainly derived from group-based ("nomothetic") studies that yield group-aggregated results, while often the need is to answer questions that apply to individuals. Particularly in the presence of great inter-individual differences and temporal complexities, information at the individual-person level may be valuable for personalized treatment decisions, individual predictions and diagnostics. The single-subject study design can be used to make inferences about individual persons. Yet, the single-subject study is not often used in the field of psychiatry. We believe that this is because of a lack of awareness of its value rather than a lack of usefulness or feasibility. In the present paper, we aimed to resolve some common misconceptions and beliefs about single-subject studies by discussing some commonly heard "facts and fictions." We also discuss some situations in which the single-subject study is more or less appropriate, and the potential of combining single-subject and group-based study designs into one study. While not intending to plea for single-subject studies at the expense of group-based studies, we hope to increase awareness of the value of single-subject research by informing the reader about several aspects of this design, resolving misunderstanding, and providing references for further reading

Temporal associations between salivary cortisol and emotions in clinically depressed individuals and matched controls:A dynamic time warp analysis

Depression can be understood as a complex dynamic system where depressive symptoms interact with one another. Cortisol is suggested to play a major role in the pathophysiology of depression, but knowledge on the temporal interplay between cortisol and depressive symptoms is scarce. We aimed to analyze the temporal connectivity between salivary cortisol and momentary affective states in depressed individuals and controls. Thirty pair-matched depressed and non-depressed participants completed questionnaires on momentary positive (PA) and negative (NA) affect and collected saliva three times a day for 30 days. The association between cortisol and affect was analyzed by dynamic time warp (DTW) analyses. These analyses involved lag-1 backward to lag-1 forward undirected analyses and lag-0 and lag-1 forward directed analyses. Large inter- and intra-individual variability in the networks were found. At the group level, with undirected analysis PA and NA were connected in the networks in depressed individuals and in controls. Directed analyses indicated that increases in cortisol preceded specific NA items in controls, but tended to follow upon specific affect items increase in depressed individuals. To conclude, at group level, changes in cortisol levels in individuals diagnosed with a depression may be a result of changes in affect, rather than a cause.</p