Search

Rumination describes a form of repetitive negative thinking (RNT) that is defined as “a mode of responding to distress that involves repetitively and passively focussing on symptoms of distress and the possible causes and consequences of these symptoms” (Nolen-Hoeksema et al., 2008). It is associated with the onset, duration, and severity of depression, which is one of the most common and burdensome mental disorders in Germany (Grobe & Steinmann, 2020). Investigating ruminative processes will lead to a deeper understanding of an important triggering and maintaining factor of depression. Rumination is so far usually measured via self-report, which is prone to cognitive and response biases, and the assessment might interfere with the process itself (e.g. asking if one is ruminating might lead to rumination itself). Thus, this project aims at developing a more objective and valid neurophysiological measurement of rumination: machine learning algorithms learn to predict rumination versus distraction from rumination (in short: distraction) based on neurophysiological EEG data patterns (64 channels). To generate training data, rumination and distraction are experimentally induced in depressed patients as previously shown (e. g. Ehring, Fuchs & Kläsener, 2009; Huffziger & Kühner, 2009) in a randomised within-subject design. Individual decoders will be trained using a classification model to classify the two mental states (i.e., rumination vs. distraction) based on EEG data patterns. We hypothesize that the decoder can differentiate rumination from distraction with a decoding accuracy significantly above chance level. The project will establish a more objective measurement for rumination that will enable future studies to investigate rumination with less cognitive biases and higher temporal resolution than self-reports.

Rumination is associated with the onset, duration and severity of a depression. Being distracted from ruminative thoughts (“distraction hypothesis”) is discussed as a possible mechanism of action of the antidepressant effect of moderate to vigorous exercise, which is well-established (Heissel et al., 2023; Morres et al., 2019). In this project, we decode rumination from electroencephalography (EEG) data using machine learning algorithms. Decoded rumination and self-reports are used to predict possible changes in rumination through exercise. Decoded rumination provides a more objective measure of rumination, additional to and beyond self-reports, that might be less biased and shed light into the underlying neurophysiological correlates of rumination. In this project, we will investigate whether moderate-intensity exercise (ME) reduces rumination compared to a sedentary control condition (SED). ME will be performed as continuous exercise at 100-110% of the individual first lactate threshold. In the sedentary control condition, participants sit inactive in a chair. Each condition is performed for 30 minutes. Participants will complete a single factor (ME vs. SED) within-subject design in randomised order while EEG is measured. EEG is applied with 59 electrodes according to the 10-20 system. Additionally, data is measured from 4 EOG electrodes, 1 electrode at muscle risorius, 4 bipolar electrodes at muscle trapezius and 4 bipolar electrodes at sternocleidomastoid muscle. In a previous part of the project (https://doi.org/10.17605/OSF.IO/C5JF9), decoders (i.e., support-vector classification models) are trained to predict rumination (versus distraction) from EEG data during experimentally induced rumination or distraction. In the current project, the trained decoders predict the class (i.e., rumination vs. distraction) and class probability of rumination from continuous EEG data features (i.e., alpha and theta power across the 59 channels and a connectivity matrix between all channels) measured during the exercises. The class probability for rumination is analysed in 7.5 s data segments across the time course of ME or SED, respectively. Furthermore, self-reported rumination will be assessed before and after each condition using the Perseverative Thinking Questionnaire state (PTQ-S; Ehring et al., 2011) and during the conditions using visual analogue scales (VAS). We hypothesize that the mean change of self-reported rumination as well as the mean decoded probability of rumination is significantly lower in the ME condition compared to the SED condition. By implementing a novel, more objective measurement of rumination in combination with validated and well-established self-reports, this project will help to understand whether distraction mediates the antidepressant effect of exercise.

This research question is a part of the ImPuls study. The study protocol of the ImPuls study is published at https://doi.org/10.1186/s12888-021-03541-3 and registered in the German Clinical Trial Register (ID: DRKS00024152, 05/02/2021).

These research question is a part of the ImPuls study. The study protocol of the ImPuls study is published at https://doi.org/10.1186/s12888-021-03541-3 and registered in the German Clinical Trial Register (ID: DRKS00024152, 05/02/2021). We know from literature that there are a lot of variables that may have an impact on the symptom severity in a transdiagnostic sample of different mental disorders. In the following section we will shortly present possible impact factors that are also assumed to be mechanisms of change within the pragmatic randomized controlled trial (RCT). Physical activity (PA) and PA-related health competence (PAHCO). There is a body of evidence assuming that PA that is planned, structured and repeated, with the primary aim to improve or maintain physical fitness (Caspersen et al., 1985) might represent a preventive factor as well as an effective treatment in patients suffering from mental disorders. In a recently published RCT, an exercise intervention was efficacious in improving global symptom severity in a transdiagnostic sample (Zeibig et al., 2021). The results indicate that the reduction of global symptom severity was predicted by an increase in the amount of PA (Zeibig et al., 2021). Furthermore, exercise-specific affect regulation as part of PAHCO (Schmid et al., 2020) seemed to underly treatment effects (Zeibig et al., under review). Repetitive negative thinking (RNT). RNT defined as “a mode of responding to distress that involves repetitively and passively focusing on symptoms of distress and the possible causes and consequences of these symptoms” (Nolen-Hoeksema et al., 2008) might be a transdiagnostic risk factor for severity of depression and anxiety (Spinhoven et al., 2018). Sleep quality. Sleep disturbance is a symptom of many axis I disorders, e.g. in posttraumatic stress disorder or depressive disorders. In fact, depressed patients who also suffer from insomnia report higher levels of symptom severity compared to depressed patients without insomnia (Sunderajan et al., 2010). Evidence that extends these results to patients with other psychiatric disorders indicate that impaired sleep quality is strongly associated with higher levels of psychiatric symptom severity, assessed by patients themselves and clinicians (Kallestad et al., 2012). Health-related quality of life (HRQoL). HRQoL describes the perceived physical and mental health of an individual over time and includes correlates of HRQoL as health risks, functional status and social support (Centers for Disease Control and Prevention, 2000). A general population survey revealed that depressive and anxiety disorders are associated with the poorest outcome of HRQoL (Saarni et al., 2007). Regarding symptom severity, HRQoL seems to be negatively correlated among depressive patients (Cao et al., 2013) and patients suffering from post-traumatic stress disorder (Nachar et al., 2013) as well as from insomnia (Kyle et al., 2010). Perceived stress. Stress is defined as situations “in which environmental demands, internal demands, or both, tax or exceed the adaptive resources of an individual, social system, or tissue system” (Monat & Lazarus, 1991). Acute stressful life events or chronic levels of stress are a risk factor for a broad range of mental disorders such as posttraumatic stress disorder, depression, insomnia and anxiety disorders (Basta et al., 2007; Smoller, 2016). At the same time, people suffering from mental disorders are more sensitive to stress and experience higher levels of stress than healthy people (Connor et al., 2007; Zorn et al., 2017). However, research regarding symptom severity in a clinical sample is still pending. Fear of the corona virus (FoC). During the Covid-19 pandemic, people were confronted with a new, fast-spreading virus that was associated with high mortality rates, lack of treatment methods and overstrained health systems (Meyerowitz-Katz & Merone, 2020; Wiersinga et al., 2020). This situation was associated with high FoC in the population (Luo et al., 2021) and first results suggest that FoC is positively associated with symptom severity in depression and anxiety disorders (Belen, 2021; Fink et al., 2021; Rogers et al., 2021). Emotional regulation (ER). ER is defined as the “extrinsic and intrinsic processes responsible for monitoring, evaluating, and modifying emotional reactions, especially their intensive and temporal features, to accomplish one’s goal” (Thompson, 1994). Deficits in ER may favor the development of depression (Berking et al., 2014) and among patients suffering from depression deficits are seen as one key factor in the maintenance of symptoms (Ehring et al., 2010). In turn, enhancing ER skills using specific interventions may reduce symptom severity in depressive patients (Radkovsky et al., 2014). Emotional intelligence (EI). EI is defined as „the ability to process your emotions and others’ emotions to guide your thinking and behavior” (Ahmed Okasha et al., 2022) and impaired EI is seen as one principal component in depressive disorders (Fisher et al., 2010). Thus, research implies that impaired self-perceived EI is correlated with symptom severity among depressive patients (Ahmed Okasha et al., 2022). Sociodemographic data. Among a representative adult German sample, the prevalence of common mental disorders (e.g. depression or anxiety disorders) is higher among females, except for substance use disorders (Klose & Jacobi, 2004). Furthermore, being single, separated, divorced or widowed as well as being unemployed is associated with an increased risk of any mental disorder. Regarding the prevalence of depressive or anxiety disorders, no age effects were found (Klose & Jacobi, 2004) and higher education seems to yield lower rates of mood disorders (Alonso et al., 2004). However, research regarding symptom severity in a clinical sample is still pending. To summarize we can say that there are various variables associated with symptom severity of different mental disorders. However, they are mainly investigated in disorder-specific samples which is not in line with growing evidence supporting transdiagnostic treatments in patients with mental disorders (Dalgleish et al., 2020). Thus, research regarding impact factors on global symptom severity in a transdiagnostic sample is necessary. These results could provide important information that could be used to create transdiagnostic therapy approaches focusing on those impact factors with the highest impact on global symptom severity.

Everyday people are confronted with the perception of stress caused by several problems and challenges. These so-called stressors reach from long lists of tasks at work over the car traffic at rush hour up to social conflicts at home. Furthermore, crises of world affairs such as the Covid-19 pandemic are additional stressors, associated with a high prevalence of stress among the general population (Salari et al., 2020). Stress is defined as situation “in which environmental demands, internal demands, or both, tax or exceed the adaptive resources of an individual, social system, or tissue system,” (Monat & Lazarus, 1991). Acute stressful life events or chronic levels of stress are a risk factor for a broad range of mental disorders such as posttraumatic stress disorder (PTSD), major depressive disorder (MDD), Insomnia and anxiety disorders (Basta et al., 2007; Smoller, 2016). At the same time, people suffering from mental disorders are more sensitive to stress and experience higher levels of stress than healthy people (Connor et al., 2007; Zorn et al., 2017). Therefore, these mental disorders are also called stress-related disorder (Smoller, 2016). Stress does not necessarily lead to the development of a mental disorder, but it is generally associated with a lower quality of life (QOL) (Chaudhury et al., 2018). QOL is defined as the “individuals' perception of their position in life in the context of the culture and value systems in which they live and in relation to their goals, expectations, standards and concerns” (WHOQOL, 1998, p. 1570). According to a systematic review the negative association between stress and QOL becomes even stronger if people also suffer from disorders such as sleep disorders, burnout or depression (Ribeiro et al., 2018). However, studies mainly compared stress and QOL from samples consisting of individuals with a specific mental disorder with healthy controls (Berghofer et al., 2020; Zorn et al., 2017). Although both constructs are shared relevant factors across all stress-related disorders, there is a gap in data from transdiagnostic analyses. One part of this study is therefore to fill this gap. Physical activity (PA), which is “any bodily movement produced by skeletal muscles that results in energy expenditure” (Caspersen et al., 1985, p. 128), has a contrary effect to stress on mental health in that it directly reduces the experience of stress but also elevates mood and increases QOL not only in healthy people but also in people suffering from mental disorders such as depression or anxiety disorder (Penedo & Dahn, 2005; White et al., 2017). The latter mechanism is attributed to the so-called stress-buffering hypothesis. According to this hypothesis, the increase, or a reduced decrease – compared to stronger decreases – in characteristics such as QOL is due to a reduction in the negative impact of stress (Gerber & Puhse, 2009). In one of the studies investigating the effect university students with more PA have been shown to be more satisfied in life and having higher positive affect than the group with lower levels of PA although they were confronted with the same academical stressors during this period (Wunsch et al., 2017). However, there is a lack of data on this hypothesis with regard to people suffering from mental disorders as – to our knowledge – it has only been tested on healthy people with provoked or naturally occurring stressors or people with physical diseases (Giessing et al., 2021; Klaperski & Fuchs, 2021; Schilling et al., 2020). Especially in terms of the underlying transdiagnostic mechanisms already mentioned, the idea of a stress buffer through PA does not seem to have been explored yet. Hence, this context gives rise to tow objectives for this study. In a first step we want to show that stress and QOL can be seen as underlying transdiagnostic factors in people with stress-related disorders. We expect higher levels of stress among the transdiagnostic sample and we also suspect that this group shows lower QOL due to the negative association of these two factors than the matched healthy sample. The second and also the main aim of this study is to shed light on the impact of PA on the association between stress and QOL in a transdiagnostic sample of people suffering from depression (mild, severe), insomnia, post-traumatic stress disorder and/or panic disorder (with or without agoraphobia).

Additional file 1. TIDieR-Checklist.pdf (TIDieR Checklist).