The COVID-19 pandemic has potentially increased the risk for adolescent depression. Even pre-pandemic, <50% of youth with depression accessed care, highlighting needs for accessible interventions. Accordingly, this randomized controlled trial (ClinicalTrials.gov: NCT04634903) tested online single-session interventions (SSIs) during COVID-19 in adolescents with elevated depression symptoms (N = 2,452, ages 13-16). Adolescents from all 50 US states, recruited via social media, were randomized to one of three SSIs: a behavioural activation SSI, an SSI teaching that traits are malleable and a supportive control. We tested each SSI's effects on post-intervention outcomes (hopelessness and agency) and three-month outcomes (depression, hopelessness, agency, generalized anxiety, COVID-19-related trauma and restrictive eating). Compared with the control, both active SSIs reduced three-month depressive symptoms (Cohen's d = 0.18), decreased post-intervention and three-month hopelessness (d = 0.16-0.28), increased post-intervention agency (d = 0.15-0.31) and reduced three-month restrictive eating (d = 0.12-17). Several differences between active SSIs emerged. These results confirm the utility of free-of-charge, online SSIs for high-symptom adolescents, even in the high-stress COVID-19 context.
The United States spends more money on mental health services than any other country, yet access to effective psychological services remains strikingly low. The need-to-access gap is especially wide among children and adolescents, with up to 80% of youths with mental health needs going without services, and the remainder often receiving insufficient or untested care. Single-session interventions (SSIs) may offer a promising path toward improving accessibility, cost-effectiveness, and completion rates for youth mental health services. SSIs are structured programs that intentionally involve only one visit or encounter with a clinic, provider, or program; they may serve as stand-alone or adjunctive clinical services. A growing body of evidence supports the capacity of SSIs to reduce and prevent youth psychopathology of multiple types. Here, we provide a working definition of SSIs for use in future research and practice; summarize the literature to date on SSIs for child and adolescent mental health; and propose recommendations for the future design, evaluation, and implementation of SSIs across a variety of settings and contexts. We hope that this paper will serve as an actionable research agenda for gauging the full potential of SSIs as a force for youth mental health.
Objective: Experiencing depression symptoms, even at mild to moderate levels, is associated with maladaptive outcomes for adolescents. We used network analysis to evaluate which symptoms (and associations between symptoms) are most central to adolescent depression.Method: Participants were part of a large, diverse community sample (N = 1,409) of adolescents between the ages of 13-19 years old. Network analysis was used to identify the most central symptoms (nodes) and associations between symptoms (edges) assessed by the Children’s Depression Inventory (CDI). We also evaluated these centrality indicators for network robustness using stability and accuracy tests, associated symptom centrality with mean levels of symptoms, and examined potential differences between the structure and connectivity of depression networks in boys and girls. Results: The most central symptoms in the network were self-hatred, loneliness, sadness, and pessimism. The strongest associations between symptoms were sadness-crying, anhedonia-school dislike, sadness-loneliness, school work difficulty-school performance decrement, self-hatred-negative body image, sleep disturbance-fatigue, and self-deprecation-self-blame. The network was robust to stability and accuracy tests. Notably, symptom centrality and mean levels of symptoms were not associated. Boys and girls’ networks did not differ in levels of connectivity, though the link between body image and self-hatred was stronger in girls than boys. Conclusions: Self-hatred, loneliness, sadness, and pessimism were the most central symptoms in adolescent depression networks, suggesting these symptoms (and associations between symptoms) should be prioritized in theoretical models of adolescent depression and could also serve as important treatment targets for adolescent depression interventions.
Cognitive models of depression posit that negatively biased self-referent processing and attention have important roles in the disorder. However, depression is a heterogeneous collection of symptoms and it is unlikely that all symptoms are associated with these negative cognitive biases. The current study involved 218 community adults whose depression ranged from no symptoms to clinical levels of depression. Random forest machine learning was used to identifythe most important depression symptom predictors of each negative cognitive bias. Depression symptoms were measured with the Beck Depression Inventory – II. Performance of models was evaluated using predictive R-squared (𝑅2 𝑝𝑟𝑒𝑑), the expected variance explained in data not used to train the algorithm, estimated by 10 repetitions of 10-fold cross-validation. Using the Self- Referent Encoding Task (SRET), depression symptoms explained 34% to 45% of the variance in negative self-referent processing. The symptoms of sadness, self-dislike, pessimism, feelings of punishment, and indecision were most important. Notably, many depression symptoms made virtually no contribution to this prediction. In contrast, for attention bias for sad stimuli, measured with the dot-probe task using behavioral reaction time and eye gaze metrics, no reliable symptom predictors were identified. Findings indicate that a symptom-level approach may provide new insights into which symptoms, if any, are associated with negative cognitive biases in depression. General Scientific Summary: This study finds that many symptoms of depression are not strongly associated with thinking negatively about oneself or attending to negative information. This implies that negative cognitive biases may not be strongly associated with depression per se, but may instead contribute to the maintenance of specific depression symptoms, such as sadness, self-dislike, pessimism, feelings of punishment, and indecision.
Although depression symptoms are often treated as interchangeable, some symptoms may relate to adolescent functioning more strongly than others. To assess this premise, we first conducted a network analysis on the Mood and Feelings Questionnaire (MFQ) in a large (N = 1,059), cross-sectional sample of community adolescents (age M = 14.72 ± 1.79). The most central symptoms of adolescent depression, as indexed by strength, were self-hatred, loneliness, sadness, and worthlessness. Moreover, the more central a depression symptom was in the network (i.e., higher strength), the more variance it shared with life satisfaction (r = 0.59, 95% CI: 0.27, 0.76). How frequently a symptom was endorsed was negatively associated with the variance symptoms shared with life satisfaction (r = -0.48, 95% CI: -0.63, -0.21). Prediction focused models found central symptoms were expected to predict more out of sample variance in life satisfaction than peripheral symptoms and frequently endorsed symptoms, but not the least frequently endorsed symptoms. These findings show multiple symptom-level metrics might identify symptoms that most strongly predict life satisfaction in adolescence.
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