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Author(s): Amanda C. Collins [sup.1] [sup.2] [sup.3] , Alisson N. S. Lass [sup.4] , E. Samuel Winer [sup.5] Author Affiliations: (1) https://ror.org/0432jq872, grid.260120.7, 0000 0001 0816 8287, Department of Psychology, [...], Depressed individuals hold negative schemas and experience less positivity in their lives. Network analyses suggest that this may be due to connectivity among negative concepts within depressed individuals' schemas. However, the extent to which positivity interacts with negativity in depressed persons' schemas has not been thoroughly assessed. Thus, we examined the interactive components of the Fear of Happiness Scale, a construct that examines negative emotions associated with happiness, in 966 individuals with depressive symptoms ranging from none to severe via a moderated network model. Patterns of connectivity differed by depressive symptom severity. Notably, as depressive symptoms increased, items representing avoidance and dampening of positivity with negative thoughts went from weakly, negatively connected at no depressive symptoms to strongly, positively connected at elevated depressive symptoms. In other words, individuals who experience higher levels of depressive symptoms devalue positivity in their semantic networks, demonstrating an opposite pattern to those without depressive symptoms. Future work should longitudinally examine other individual differences related to the devaluation of reward to ascertain how these individual differences directly contribute to and maintain depressive symptoms.

The presentation of major depressive disorder (MDD) can vary widely due to its heterogeneity, including inter- and intraindividual symptom variability, making MDD difficult to diagnose with standard measures in clinical settings. Prior work has demonstrated that passively collected actigraphy can be used to detect MDD at a disorder level; however, given the heterogeneous nature of MDD, comprising multiple distinct symptoms, it is important to measure the degree to which various MDD symptoms may be captured by such passive data. The current study investigated whether individual depressive symptoms could be detected from passively collected actigraphy data in a (a) clinical subpopulation (i.e., moderate depressive symptoms or greater) and (b) general population. Using data from the National Health and Nutrition Examination Survey, a large nationally representative sample (N = 8,378), we employed a convolutional neural network to determine which depressive symptoms in each population could be detected by wrist-worn, minute-level actigraphy data. Findings indicated a small-moderate correspondence between the predictions and observed outcomes for mood, psychomotor, and suicide items (area under the receiver operating characteristic curve [AUCs] = 0.58–0.61); a moderate-large correspondence for anhedonia (AUC = 0.64); and a large correspondence for fatigue (AUC = 0.74) in the clinical subpopulation (n = 766); and a small-moderate correspondence for sleep, appetite, psychomotor, and suicide items (AUCs = 0.56–0.60) in the general population (n = 8,378). Thus, individual depressive symptoms can be detected in individuals who likely meet the criteria for MDD, suggesting that wrist-worn actigraphy may be suitable for passively assessing these symptoms, providing important clinical implications for the diagnosis and treatment of MDD. General Scientific Summary: The coupling of deep learning methods with passive monitoring of an individual's naturalistic movement provides a unique opportunity to detect depressive symptoms without the necessity for frequent clinical visits or self-report measures. The present work builds upon previous efforts to evaluate which depressive symptoms are best captured by passively collected physical activity data, and how this differs between individuals in the general population and individuals who meet criteria for depression. Our findings provide insight into which individual depressive symptoms may be best detected by passively collected physical activity data, providing important assessment and treatment implications for depression. [ABSTRACT FROM AUTHOR]

Objectives: Each person possesses a unique view surrounding depressive symptomology and etiology that is shaped by idiosyncratic experiences. However, the influence that subjective etiological beliefs regarding a person's depressive symptoms have on actual symptom presentation and organization is seldom considered. Methods: The current study employed network analytic techniques to examine how subjective views surrounding the cause of depressive symptoms altered actual symptom presentation networks. Additionally, the interaction between depressive symptoms and various etiological beliefs was examined. Results: The results revealed that characterological beliefs, representing the idea that depression is caused by an internal sense of self, are strongly connected to a negative view of self, as well as a saddened mood. Additionally, the characterological beliefs node exhibited the greatest node predictability in its respective network, as well as in an omnibus network consisting of all depression symptoms and potential etiological beliefs. Whereas an achievement‐based view of depression has a strong connection with concentration difficulties, a physical view of depression tends to form strong connections with physically based depressive symptoms. Conclusion: Subjective views regarding the cause of depression have the potential to influence symptom presentation and organization within a network, which may influence a person's willingness to engage in treatment or specific treatment preferences. [ABSTRACT FROM AUTHOR]

Positive psychology interventions (PPIs) are effective at increasing happiness and decreasing depressive symptoms. PPIs are often administered as self-guided web-based interventions, but not all persons benefit from web-based interventions. Therefore, it is important to identify whether someone is likely to benefit from web-based PPIs, in order to triage persons who may not benefit from other interventions. In the current study, we used machine learning to predict individual response to a web-based PPI, in order to investigate baseline prognostic indicators of likelihood of response (N = 120). Our models demonstrated moderate correlations (happiness: rTest = 0.30 ± 0.09; depressive symptoms: rTest = 0.39 ± 0.06), indicating that baseline features can predict changes in happiness and depressive symptoms at a 6-month follow-up. Thus, machine learning can be used to predict outcome changes from a web-based PPI and has important clinical implications for matching individuals to PPIs based on their individual characteristics. [ABSTRACT FROM AUTHOR]

Major depressive disorder (MDD) is conceptualized by individual symptoms occurring most of the day for at least two weeks. Despite this operationalization, MDD is highly variable with persons showing greater variation within and across days. Moreover, MDD is highly heterogeneous, varying considerably across people in both function and form. Recent efforts have examined MDD heterogeneity byinvestigating how symptoms influence one another over time across individuals in a system; however, these efforts have assumed that symptom dynamics are static and do not dynamically change over time. Nevertheless, it is possible that individual MDD system dynamics change continuously across time. Participants (N = 105) completed ratings of MDD symptoms three times a day for 90 days, and we conducted time varying vector autoregressive models to investigate the idiographic symptom networks. We then illustrated this finding with a case series of five persons with MDD. Supporting prior research, results indicate there is high heterogeneity across persons as individual network composition is unique from person to person. In addition, for most persons, individual symptom networks change dramatically across the 90 days, as evidenced by 86% of individuals experiencing at least one change in their most influential symptom and the median number of shifts being 3 over the 90 days. Additionally, most individuals had at least one symptom that acted as both the most and least influential symptom at any given point over the 90-day period. Our findings offer further insight into short-term symptom dynamics, suggesting that MDD is heterogeneous both across and within persons over time. General Scientific Summary: In the current study, we conducted a case series on five individuals with current major depressive disorder (MDD) who responded to a depression questionnaire three times a day and used a time-series methodology to determine, for each person, how each of the symptoms changed and influenced each other over the course of 90 days. Our findings indicate that MDD is not only heterogeneous in its manifestation between persons but can also be highly variable in presentation within an individual over time. As such, individuals with MDD who experience a more variable symptom presentation may benefit from personalized, time-sensitive clinical interventions. [ABSTRACT FROM AUTHOR]

Some individuals devalue positivity previously associated with negativity (Winer & Salem, 2016). Positive emotions (e.g. happiness) may be seen as threatening and result in active avoidance of future situations involving positivity. Although some self-report measures can capture emotions of happiness-averse individuals, they are not always capable of capturing automatic processing. Thus, we examined the association between implicitly-assessed happiness and explicit (i.e. self-reported) fear of happiness in three studies. In Study 1, participants completed the Fear of Happiness Scale (FHS) and an implicit measure of emotions at four-time points over approximately one year. The implicit measure required participants to choose which emotion (i.e. anger, fear, happiness, sadness, or none) best corresponded to 20 individual Chinese characters. In Studies 2 and 3, we utilized an experimental design, implementing a mood induction to emphasise the relationship between explicit fear of happiness and implicitly-assessed happiness. Participants completed the FHS and chose which emotion they believed the artist tried to convey in 20 abstract images. Results indicated that greater self-reported fear of happiness was related to reduced implicit happiness. Findings from these studies provide compound evidence that individuals who hold negative views of positivity may process implicit happiness in a devaluative manner. [ABSTRACT FROM AUTHOR]

Depressive symptoms have been shown to be negatively related to academic achievement, as measured by grade point average (GPA). Grit, or the passion for and the ability to persevere toward a goal despite adversity, has been linked to GPA. Thus, grit may potentially buffer against the negative effects of depressive symptoms in relation to academic achievement. However, social desirability may might impact the validity of grit when assessed by self-report measures, so how these constructs are all related is unknown. The current study explored the relationship between depressive symptoms, grit, social desirability, and GPA among University students (N = 520) in the United States using a cross-sectional design. We conducted a moderated-moderation model to examine how social desirability moderated the relationship between depressive symptoms, grit, and GPA. Findings replicated prior work and indicated negative relationships between depressive symptoms and social desirability with GPA and a positive relationship, albeit non-significant, between grit and GPA. However, results suggest that grit did not moderate the relationship between depressive symptoms and GPA when including social desirability in the model. Future research should investigate this relationship in a longitudinal setting to further examine how grit and depressive symptoms influence one another in academic domains. [ABSTRACT FROM AUTHOR]

Objective: Components of rumination, including brooding and reflection, as well as devaluating prospective positivity, may help maintain depressive symptoms. We examined these components together for the first time using network analysis. Methods: We examined the robustness of rumination communities of closely related items in one network and then examined the interrelationships between rumination communities, devaluation of positivity, and depression, in a second network. Results: Three rumination communities emerged, replicating findings of Bernstein et al. (2019). Within a dense network, nodes representing brooding, reflective pondering, and difficulty trusting positive feelings were most influential. In addition, the node representing the depressive symptom negative self‐views shared strong edges with nodes representing devaluation of positivity and brooding. Conclusion: Brooding, reflective pondering, and elements of devaluing positivity are influential to depressive symptoms and may be important future experimental and therapeutic targets. Depressed individuals with negative self‐views may engage in brooding and devalue their experience of positivity. [ABSTRACT FROM AUTHOR]

Objectives: Reward devaluation theory (RDT) posits that some depressed individuals avoid positivity due to its previous association with negative outcomes. Behavioral indicators of avoidance of reward support RDT, but self‐report indicators have yet to be examined discriminantly. Two candidate self‐report measures were examined in relation to depression: negative affect interference (NAI), or the experience of negative affect in response to positivity, and fear of happiness, a fear of prospective happiness. Method: Participants completed measures assessing NAI, fear of happiness scale, and depression online via Amazon's Mechanical Turk at three time points (N = 375). Multilevel modeling examined the relationship between NAI, fear of happiness, and depressive symptoms longitudinally. Results: NAI and fear of happiness were both positively associated with depressive symptoms. They both uniquely predicted depressive symptoms when included within the same model. Conclusions: These findings suggest that different conceptualizations of positivity avoidance are uniquely associated with depressive symptoms. [ABSTRACT FROM AUTHOR]

Background: Existing interventions for co-occurring depression and cannabis use often do not treat both disorders simultaneously and can result in higher rates of symptom relapse. Traditional in-person interventions are often difficult to obtain due to financial and time limitations, which may further prevent individuals with co-occurring depression and cannabis use from receiving adequate treatment. Digital interventions can increase the scalability and accessibility for these individuals, but few digital interventions exist to treat both disorders simultaneously. Targeting transdiagnostic processes of these disorders with a digital intervention-specifically positive valence system dysfunction-may yield improved access and outcomes., Objective: Recent research has highlighted a need for the inclusion of individuals with lived experiences to assist in the co-design of interventions to enhance scalability and relevance of an intervention. Thus, the purpose of this study is to describe the process of eliciting feedback from individuals with elevated depressed symptoms and cannabis use and co-designing a digital intervention, Amplification of Positivity-Cannabis Use Disorder (AMP-C), focused on improving positive valence system dysfunction in these disorders., Methods: Ten individuals who endorsed moderate to severe depressive symptoms and regular cannabis use (2-3×/week) were recruited online via Meta ads. Using a mixed methods approach, participants completed a 1-hour mixed methods interview over Zoom (Zoom Technologies Inc) where they gave their feedback and suggestions for the development of a mental health app, based on an existing treatment targeting positive valence system dysfunction, for depressive symptoms and cannabis use. The qualitative approach allowed for a broader investigation of participants' wants and needs regarding the engagement and scalability of AMP-C, and the quantitative approach allowed for specific ratings of intervention components to be potentially included., Results: Participants perceived the 13 different components of AMP-C as overall helpful (mean 3.9-4.4, SD 0.5-1.1) and interesting (mean 4.0-4.9, SD 0.3-1.1) on a scale from 1 (not at all) to 5 (extremely). They gave qualitative feedback for increasing engagement in the app, including adding a social component, using notifications, and being able to track their symptoms and progress over time., Conclusions: This study highlights the importance of including individuals with lived experiences in the development of interventions, including digital interventions. This inclusion resulted in valuable feedback and suggestions for improving the proposed digital intervention targeting the positive valence system, AMP-C, to better match the wants and needs of individuals with depressive symptoms and cannabis use., (©Amanda C Collins, Sukanya Bhattacharya, Jenny Y Oh, Abigail Salzhauer, Charles T Taylor, Kate Wolitzky-Taylor, Robin L Aupperle, Alan J Budney, Nicholas C Jacobson. Originally published in JMIR Formative Research (https://formative.jmir.org), 07.10.2024.)

MoodCapture presents a novel approach that assesses depression based on images automatically captured from the front-facing camera of smartphones as people go about their daily lives. We collect over 125,000 photos in the wild from N=177 participants diagnosed with major depressive disorder for 90 days. Images are captured naturalistically while participants respond to the PHQ-8 depression survey question: "I have felt down, depressed, or hopeless" . Our analysis explores important image attributes, such as angle, dominant colors, location, objects, and lighting. We show that a random forest trained with face landmarks can classify samples as depressed or non-depressed and predict raw PHQ-8 scores effectively. Our post-hoc analysis provides several insights through an ablation study, feature importance analysis, and bias assessment. Importantly, we evaluate user concerns about using MoodCapture to detect depression based on sharing photos, providing critical insights into privacy concerns that inform the future design of in-the-wild image-based mental health assessment tools.