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Weather Forecasting in Depression: Is it Possible to Detect Early Warning Signs of Depression?

In the IDA course "Dynamics of Individual Differences", students write a blog post on a topic of their interest that is related to an aspect that differs between individuals and changes across the lifespan. Enjoy reading the blog post of Annelies!

“Because wherever I sat – on the deck of a ship or at a street café in Paris or Bangkok – I would be sitting under the same glass bell jar, stewing in my own sour air.” ~ The Bell Jar, Sylvia Plath

What is Depression?

This quotation highlights the feeling of being trapped inside yourself, that characterizes depression. Most people feel sad, lonely, or even depressed at times. This is a normal reaction to the ups and downs of life, such as loss, personal struggles, or negative thinking patterns. Experiencing negative emotions can even be adaptive in some situations. For example, “deliberate rumination” after stressful events is a form of self-reflection and involves trying to purposely make sense of what is happening, resulting in enhanced cognition (Cova et al., 2019; Kamijo & Yukawa, 2018). However, although negative emotions may sometimes be beneficial, when these feelings of sadness, helplessness, hopelessness, and worthlessness last for a long period of time and withheld you from your functioning in daily life, they could turn into a negative spiral and eventually turn into a depression. Depression is a common illness worldwide – affecting around 300 million people of all ages (World Health Organization, 2021) – yet it still eludes complete understanding.

Current Challenges in Terms of Treatment and Diagnosis

Effective treatments are available, including antidepressant medication and psychotherapy. However, although progress has been made for some people, there is still a great room for improvement (Cuijpers et al., 2020). The mechanisms that underly depression remain quite unclear, although a considerable number of studies have investigated the biological underpinnings in an attempt to create a better understanding of depressive disorders (Kapur et al., 2012). Such lack of understanding may arise from the fact that most studies have investigated depression at the disorder level (Manfro et al., 2021). The Diagnostic and Statistical Manual (5th edition; DSM-5), which is intensively used in clinical practice to diagnose psychopathological disorders, requires the presence of 5 out of 9 symptoms, allowing for over 200 symptom presentations that meet the current diagnosis of a Major Depressive Disorder (MDD). Indeed, such heterogeneity might be difficult to account for in existing treatment methods, since most treatment methods offer a “one size fits all” approach, thereby not considering individual differences (Manfro et al., 2021). For example, it might be the case that two individuals who are both receiving treatment for depression have no symptoms in common with each other, due to the large heterogeneity and seemingly endless possibilities in symptom presentations.

Common Cause Model Versus Complex Dynamic Systems

Several approaches have been considered in terms of defining depression. The common cause model views depressive symptoms as distinct entities, that arise from a common cause, depression in this case. This theory assumes that depression is a categorical construct with symptoms that are interchangeable (Fried et al., 2022a). Viewed in this way, symptoms of disorder are summed up and this total sum score reflects depression, the ‘latent’ variable. This might sound complex, but it basically means that according to this model, depression cannot directly be observed and has to be inferred from observable symptoms (Nuijten et al., 2016). Let’s use a more common instrument to illustrate this theory. Intelligence (unobservable and therefore latent) is inferred from answers in an IQ test (directly observable). The scores are added up and – after correcting for age – consequently result in an estimate of the IQ of that individual. Although this model may sound promising, one must be careful and critical while interpreting this idea. For example, such a theory disregards the fact that one cannot observe depression independent of their symptoms. Psychological disorders are not the same as for example a brain tumor, which can be observed without knowing that a patient has previously had severe headaches caused by the tumor. This latent variable approach also doesn’t provide insights into how we can predict which individuals are vulnerable to develop depression and which are not (Nuijten et al., 2016).

It therefore seems necessary to consider another approach as well. According to the approach that considers depression as a complex dynamic system, symptoms are associated with each other because they influence each other. Imagine for example Kevin. He has been up all night ruminating about his day. Because he didn’t sleep, he will feel more fatigued than before, and the next day he will experience more difficulties concentrating on his daily tasks. Following this line of reasoning, symptoms interact with each other (e.g., insomnia leads to fatigue, fatigue leads to concentration problems). Eventually, if there are multiple interactions between symptoms, which fulfill the requirement for 5 out of the 9 symptoms in the DSM-5, this is sufficient to be classified as a depressive episode. This perspective could also explain different presentations of symptoms in different people. For example, if Denise went through a breakup, which causes her to lose her appetite, this might lead to weight loss. This causes her to have less energy than usual. These symptoms influence each other and might also ultimately qualify as a depressive episode, even though Denise has no symptoms in common with Kevin. This system can be viewed as dynamic because the relations between the individual symptoms are assumed to be changing within an individual over time (Cramer et al., 2016).

Individual Differences in Vulnerability to Depression

However, although this approach sounds promising in uncovering dynamic relationships between depressive symptoms, it does not necessarily provide insights into what it is that makes certain people vulnerable for developing depression. For example, it might be the case that Kevin has to experience at least four sleepless nights to feel fatigued, whereas at the same time, one sleepless night might be enough for Denise to feel tired the next day. In this example, the connection between insomnia and fatigue seems to be stronger for Denise than for Kevin. These individual differences in vulnerability can be illustrated using the analogy of domino tiles. When domino tiles stand closely together (analogous for strong connections between symptoms), there is a high chance that when one domino tile is toppled, the other domino tiles will also be toppled. When looking at depressive symptoms in a vulnerable network, if one symptom is activated, it is very likely that other symptoms will also be activated as a result. However, when domino tiles have more distance between them, it is less likely that one toppled domino tile will result in a cascade of other toppling tiles. In a depressive symptom network with relatively weak connections, when a symptom is activated, this will less likely result in activation of other symptoms, because the relationships between the different symptoms are not that strong (Cramer et al., 2016).

Or is a Hybrid Approach More Suitable?

But maybe we could also argue that a strict distinction between common cause models and network models oversimplifies the dynamic complexity of psychological disorders. Maybe it is a combination of both common causes and networks that could help explain depression. It could be that the initial onset of depressive symptoms is explained by exposure to an adverse life event, such as losing a loved one, or chronic stress. And more stable individual differences in for example negative thinking patterns, or biased interpretations of social situations, which change much slower over time, should also be considered. These stressors might initially lead to depressive symptoms (reflecting the common cause model), but maintenance of these symptoms is more likely due to direct interactions between the symptoms (which is in accordance with the network model). However, one can also hypothesize that different adverse life events might lead to different symptom presentations. For example, losing a loved one probably activates different depressive symptoms than losing a job or a divorce (Fried & Cramer, 2017), indicating that life events that are more specific to a life stage might lead to different symptom presentations across the lifespan.

Importance of Prevention and Identifying People at Risk

These findings highlight the dynamic nature of depression. If these hybrid models could indeed shed some light on dynamic processes both within and between persons, there might be possibilities for detecting warning signals for transitions into depression, or signals relating to the period between the appearance of initial symptoms and the full development of a depressive disorder, the so-called prodromal phase. Individual differences in the way this prodrome unfolds over time need to be investigated to provide insights into which people are at risk for developing depression, and which people are not (Fried et al., 2022b). As mentioned before, improvements in treatment methods seem to lag behind. Therefore, prevention of depression, that is stopping depression before it even occurs, seems crucial in order to make a real difference in people’s lives and contribute to better well-being on a global level. Preventing depression is only possible when individuals at risk for developing depression in the near future can be detected (Fried et al., 2022b). But the question is, is it really possible to predict depressive symptoms before they have even occurred? And how could identifying those at risk for depression lead to prevention of symptoms?

WARN-D: Weather Forecasting Depressive Symptoms?

As previously discussed, depression is very heterogeneous, complex, and dynamic. To investigate depression as emerging from a system, it seems necessary to gain more insights into its development, as well as relations between the symptoms in this system (Fried et al., 2022b). Psychopathology such as depressive symptoms might be the result of a complex dynamic interplay between more moment-to-moment experiences and long-term behavioral patterns (Wichers, 2014). There is a lot of research that investigates depression at the “macro- level”. This basically means that mental disorders, like depression, represent distinct entities, that are present or absent. You either have depression or you do not. However, although this approach might have identified more general risk factors of depression, it does not tell us much about how these factors are related to depression, or how and when we should start intervening to prevent depression. It therefore seems crucial to emphasize the importance of investigating these moment-to-moment experiences and focus on the smaller units of fluctuations in symptoms. These smallest building blocks, which can be identified at the “micro-level”, represent momentary experiences in the flow of daily life (Wichers, 2014). These small and subtle differences between individuals in their experienced emotions and affect and their interaction with the context of daily life could help explain these more “macro-scale” individual differences in the course of depression as well.

The study WARN-D focuses on these momentary changes by assuming that depression is emerging from a system of symptoms, which they called the “human mood system”. For this study, 2000 students that are living in the Netherlands are followed for 2 years, while using smartwatches and smartphones to collect data with the aim to provide insights into their daily lives. The results of these methods of data collection could help us understand what kind of stressors students experience, and how they deal with these stressors. The focus is on uncovering systems that are close to transitioning into another state. In depression, this means for example changing from a healthy state to a disordered state. Identifying these vulnerability markers that forecast these future transitions could lead to identification of individuals who are at a high risk for developing depression (Fried et al., 2022b). In their study, they use the metaphor of weather forecasting and thunderstorms. When predicting thunderstorms, one doesn’t look at increases in thunderstorms, but at the monitored features of the entire weather system, and dynamic relations between these features. Currently, depression is best predicted by monitoring increasing depressive symptoms. However, this does not seem sufficient when one tries to prevent the disorder. WARN-D hopes that by examining dynamic relations among features in the depressive system, for example between the different symptoms, this can provide evidence of upcoming transitions within this system (Fried et al., 2022b). And, to put it in other words, this might prevent depression before it has even occurred in the first place. Doesn’t that sound fascinating?

Future Directions: Where does this lead us?

However, identifying people who are at risk for developing depression does not in itself lead to prevention. That’s where intervention methods come into play. For example, promoting physical activity may reduce the risk of developing depression (Mammen & Faulkner, 2013). Ecological momentary interventions, which allow individuals to complete tasks or exercises and directly incorporate these into their daily life, might also be an option in terms of preventing the onset of depression (Schueller et al., 2017). Nevertheless, it is safe to assume that for interventions as well, there is no clear picture of which interventions are most beneficial. Since we just discussed the large heterogeneity in symptoms, interventions will likely require a broad range of options to account for individual differences. Future research is necessary to investigate these preventive interventions for depression.

The aim of this blogpost was to shed some light on the dynamic nature of depression and offers some promising approaches to prevent depression in the near future. In conclusion, it seems more appropriate to focus on momentary changes in symptoms in a network system, than to look at depression in its entirety, which disregards individual differences.

And on a final note, after reading this blogpost, do you think that it is possible to prevent depression before it has occurred, or do you think that there is still a long way to go?

If you are interested in reading the articles mentioned in this post, references and links are provided here:

Cramer, A. O. J., van Borkulo, C. D., Giltay, E. J., van der Maas, H. L. J., Kendler, K. S., Scheffer, M., & Borsboom, D. (2016). Major depression as a complex dynamic system. PLoS ONE, 11(12).

Cova, F., Garcia, F., Oyanadel, C., Villagran, L., Páez, D., & Inostroza, C. (2019). Adaptive reflection on negative emotional experiences: Convergences and divergence between the processing-mode theory and the theory of self-distancing reflection. Frontiers in Psychology, 10.

Cuijpers, P., Stringaris, A., & Wolpert, M. (2020). Treatment outcomes for depression: challenges and opportunities. The Lancet Psychiatry, 7(11), 925-927.

Fried, E. I., & Cramer, A. O. J. (2017). Moving forward: challenges and directions for psychopathological network theory and methodology. Perspectives on Psychological Science, 12(6).

Fried, E. I., Flake, J. K., & Robinaugh, D. J. (2022). Revisiting the theoretical and methodological foundations of depression measurement. Nature Reviews Psychology, 1, 358-368.

Fried, E. I., Proppert, R., & Rieble, C. (2022). Building an early warning system for depression: rationale, objectives, and methods of the WARN-D study. PsyArXiv Preprints.

Kamijo, N., & Yukawa, S. (2018). The role of rumination and negative affect in meaning making following stressful experiences in a Japanese sample. Frontiers in Psychology, 9.

Kapur, S., Phillips, A. G., & Insel, T. (2012). Why has it taken so long for biological psychiatry to develop clinical tests and what to do about it? Molecular Psychiatry, 17(12), 1174-1179.

Mammen, G., & Faulkner, G. (2013). Physical activity and the prevention of depression: A systematic review of prospective studies. American Journal of Preventive Medicine, 45(5), 649-657.

Manfro, P. H., Pereira, R. B., Rosa, M., Cogo-Moreira, H., Fisher, H. L., Kohrt, B. A., Mondelli, V., & Kieling, C. (2021). Adolescent depression beyond DSM definition: a network analysis. European Child & Adolescent Psychiatry.

Nuijten, M. B., Deserno, M. K., Cramer, A. O. J., & Borsboom, D. (2016). Mental disorders as complex networks: An introduction and overview of a network approach to psychopathology. Clinical Neuropsychiatry, 13(4-5), 68-76.

Schueller, S. M., Aguilera, A., & Mohr, D. C. (2017). Ecological momentary interventions for depression and anxiety. Depression and Anxiety, 34(6), 540-545.

Wichers, M. (2014). The dynamic nature of depression: a new micro-level perspective of mental disorder that meets current challenges. Psychological Medicine, 44(7), 1-12.

World Health Organization. (2021, september). Depression.

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