Augmenting usual care SSRIs with cognitive behavioral therapy for insomnia to improve depression outcomes in youth: Design of a randomized controlled efficacy-effectiveness trial

Predictors of Sleep-Problem Trajectories Across Adolescence

Stress and sleep problems are significantly correlated in adolescents. Few longitudinal studies, however, have evaluated possible correlates and predictors of sleep problems at multiple points across adolescence. The current study examined the relation between stress and sleep problems across four years in a sample of adolescents who varied in risk for psychopathology. Participants included 223 adolescents (55% female) and 223 mothers (77% with a history of a mood disorder during their child's life). Youth were evaluated in grade 7 (M = 12.69 years, SD = 0.61) and again in grades 8, 9, and 11. Sleep problems were assessed as part of a clinical interview, and weekly stressful events were measured with the Life Events Interview for Adolescents. Multi-group latent growth curve analyses were conducted. Among youth whose mothers had a history of depression (high-risk), sleep problems significantly increased over time (p < .001). Second, among high-risk youth, at each time point, higher stress levels during the prior three months significantly predicted higher levels of sleep problems (p < .001). Finally, across the entire sample, at each time point a greater level of sleep problems predicted higher stress ratings a year later (p ≤ .001). Thus, stress was a significant predictor of sleep problems across multiple years of adolescence, particularly among offspring of mothers with a history of depression. Results highlight targets for preventive interventions for sleep problems in youth.

Personality, symptom, and demographic correlates of perceived efficacy of selective serotonin reuptake inhibitor monotherapy among current users with low mood: A data-driven approach

BACKGROUND

Selective serotonin reuptake inhibitors (SSRIs) are often the first-line treatment option for depressive symptoms, however their efficacy varies across patients. Identifying predictors of response to SSRIs could facilitate personalised treatment of depression and improve treatment outcomes. The aim of this study was to develop a data-driven formulation of demographic, personality, and symptom-level factors associated with subjective response to SSRI treatment.

METHODS

Participants were recruited online and data were collected retrospectively through an extensive digital mental health questionnaire. Extreme gradient boosting classification with nested cross-validation was used to identify factors distinguishing between individuals with low (n=37) and high (n=111) perceived benefit from SSRI treatment.

RESULTS

The algorithm demonstrated a good predictive performance (test AUC=.88±.07). Positive affectivity was the strongest predictor of response to SSRIs and a major confounder of the remaining associations. After controlling for positive affectivity, as well as current wellbeing, severity of current depressive symptoms, and multicollinearity, only low positive affectivity, chronic pain, sleep problems, and unemployment remained significantly associated with diminished subjective response to SSRIs.

LIMITATIONS

This was an exploratory analysis of data collected at a single time point, for a study which had a different primary aim. Therefore, the results may not reflect causal relationships, and require validation in future prospective studies. Furthermore, the data were self-reported by internet users, which could affect integrity of the dataset and limit generalisability of the results.

CONCLUSIONS

Our findings suggest that demographic, personality, and symptom data may offer a potential cost-effective and efficient framework for SSRI treatment outcome prediction.