Understanding depression

Depression has been seen as increasing across the lifespan. Today, I present three recent studies I found helpful. First, Hu, Mizrahi Lakan, Kalokerinos & Tamir (2024) published “Stuck with the Foot on the Pedal: Depression and motivated emotion regulation in daily life” in Emotion. Here’s the edited abstract:

According to cybernetic approaches, emotion regulation is motivated by the desire to reduce discrepancies between experienced and desired emotions. Yet, this assumption has rarely been tested directly in healthy or unhealthy populations. In two ecological momentary assessment studies, we monitored motivated emotion regulation in daily life in participants who varied in the severity of their depressive symptoms (Study 1; N = 173) and in clinically depressed and nondepressed participants (Study 2; N = 120). Across studies, associations between motivation in emotion regulation and discrepancies between experienced and desired emotions differed by depression. As expected, as discrepancies between experienced and desired emotions increased, individuals with lower depressive symptoms or without a clinical depression diagnosis were more motivated to regulate their emotions. In contrast, we found no evidence (Study 1) or weaker evidence (Study 2) for sensitivity to the size of the discrepancies between experienced and desired emotions among individuals with higher depressive symptoms or those diagnosed with clinical depression. These individuals were consistently motivated to regulate their emotions, regardless of the size of the discrepancies. These findings suggest that individuals prone to or suffering from depression may be less sensitive than nondepressed individuals to regulatory demands in emotion regulation. 

I have presented studies of emotion regulation before, but am especially pleased with this finding in well-designed studies. It makes sense that people with little or no depression are motivated to regulate their emotions when there is a growing discrepancy between how they feel and how they want to feel. It is also tragic that more depressed people don’t experience the same motivation to regulate their emotions. The next study looks at language. Stade, Ungar, Eichstaedt, Sherman & Ruscio (2023) published “Depression and Anxiety have Distinct and Overlapping Language Patterns: Results from a clinical interview” in Journal of Psychopathology and Clinical Science. Here are the edited abstract and impact statements:

Depression has been associated with heightened first-person singular pronoun use (I-usage; e.g., “I,” “my”) and negative emotion words. However, past research has relied on nonclinical samples and nonspecific depression measures, raising the question of whether these features are unique to depression vis-à-vis frequently co-occurring conditions, especially anxiety. Using structured questions about recent life changes or difficulties, we interviewed a sample of individuals with varying levels of depression and anxiety (N = 486), including individuals in a major depressive episode (n = 228) and/or diagnosed with generalized anxiety disorder (n = 273). Interviews were transcribed to provide a natural language sample. Analyses isolated language features associated with gold standard, clinician-rated measures of depression and anxiety. Many language features associated with depression were in fact shared between depression and anxiety. Language markers with relative specificity to depression included I-usage, sadness, and decreased positive emotion, while negations (e.g., “not,” “no”), negative emotion, and several emotional language markers (e.g., anxiety, stress, depression) were relatively specific to anxiety. Several of these results were replicated using a self-report measure designed to disentangle components of depression and anxiety. We next built machine learning models to detect severity of common and specific depression and anxiety using only interview language. Individuals’ speech characteristics during this brief interview predicted their depression and anxiety severity, beyond other clinical and demographic variables. Depression and anxiety have partially distinct patterns of expression in spoken language. Monitoring of depression and anxiety severity via language can augment traditional assessment modalities and aid in early detection. 

Using clinical interviews with individuals with varying levels of depression and anxiety, we found that some language patterns are shared by these conditions, whereas other patterns distinguish them. Depressed individuals show more I-usage (e.g., “I,” “me,” “my”) and sadness words (e.g., “low,” “sad,” “alone”), while anxious individuals use a much broader array of negative emotionality language (e.g., anxiety, stress, and counterintuitively, depression), raising implications for the understanding and automatic assessment of these conditions. 

These are large samples and a clever methodology. It seems especially helpful to recognize depression and anxiety in clients who don’t yet label themselves as such. The final study looks at depression in youth. Quinn, Liu, Cole, McCauley Diamond & Garber (2023) published “Relations among Symptoms of Depression over Time in At-risk Youth” in Journal of Psychopathology and Clinical Science. Here are the edited abstract and impact statement:

Depression consists of symptoms that may relate to each other in ways that go beyond simple co-occurrence. For example, some symptoms may precede and possibly contribute to the emergence of others. The present study examined several potential relations among the symptoms of depression. The overarching goals were to better understand how depression may unfold and to identify potential targets for intervention. The sample included 120 offspring of depressed parents. Youths’ symptoms of depression were rated across 89 weeks. First, we investigated which symptoms preceded and potentially contributed to other symptoms 1 week later. This model revealed that sleep disturbance predicted the occurrence of other symptoms (e.g., sad mood, fatigue), and the occurrence of sad mood was predicted by other symptoms (e.g., worthlessness/guilt, psychomotor symptoms, sleep disturbance). Second, we investigated the within-person question of which symptoms tended to co-occur at the same time point. This model identified sad mood, irritability, and anhedonia as symptoms that tended to co-occur with each other and with many other depressive symptoms. Third, we investigated the between-person question of which symptoms tended to co-occur when averaged across time. This model identified worthlessness/guilt, fatigue, and anhedonia as symptoms strongly associated with other depressive symptoms across people irrespective of timing. Results indicate that the relations among the symptoms of depression vary, such that some symptoms preceded others by 1 week, some symptoms occurred at the same time, and other symptoms co-occurred in individuals. This more detailed view of the connections among depressive symptoms informs our understanding of depression as a dynamic set of unique indicators.

Depression is a heterogeneous disorder consisting of several different types and combinations of symptoms. Taking a more detailed view of the relations among depressive symptoms over multiple time points may facilitate the identification of potential factors underlying the disorder. 

I’ve written before about the adverse impacts of poor sleep. Worthlessness and anhedonia also make sense as symptoms. These studies may be helpful to clinicians working with depressed people.