Cognitive Vulnerabilities as Prognostic Predictors of Acute and Follow-up Outcomes in Seasonal Affective Disorder Treatment with Light Therapy or Cognitive-Behavioral Therapy

Circadian markers as a predictor of response in the treatment of depression-A systematic review

Despite many available treatment options for depression, response rates remain suboptimal. To improve outcome, circadian markers may be suitable as markers of treatment response. This systematic review provides an overview of circadian markers that have been studied as predictors of response in treatment of depression. A search was performed (EMBASE, PUBMED, PSYCHINFO) for research studies or articles, randomized controlled trials and case report/series with no time boundaries on March 2, 2024 (PROSPERO: CRD42021252333). Other criteria were; an antidepressant treatment as intervention, treatment response measured by depression symptom severity and/or occurrence of a clinical diagnosis of depression and assessment of a circadian marker at baseline. 44 articles, encompassing 8,772 participants were included in the analysis. Although additional research is needed with less variation in types of markers and treatments to provide definitive recommendations, circadian markers, especially diurnal mood variation and chronotype, show potential to implement as response markers in the clinic.

Assessing Meaningful Improvement: Focus on the Tinnitus Functional Index

Many studies have attempted to determine methodology for interpreting change on outcome instruments that result from an intervention. The objective of these studies has been to devise methods to identify the minimal level of change that would be consistent with actual benefit perceived by the patient, and not just statistically significant change. With respect to intervention for bothersome tinnitus, the authors of the original study to develop and validate the Tinnitus Functional Index (TFI) suggested that a minimum 13-point reduction in the TFI score was likely to reflect a change perceived as meaningful to an individual. The 13-point estimation of meaningful change for an individual is appropriate for use with any adult seeking care for tinnitus. However, it cannot be relied upon in isolation to determine if an individual believes that there has been a noticeable, or meaningful improvement in their quality of life. It is important to use subjective impressions elicited from the patient to assist in interpreting the meaning of TFI data for an individual. For clinicians engaging in care for tinnitus, we recommend using the TFI and pairing it with the patient's belief/impression as to whether they are doing better than they were before care for tinnitus was provided. Ideally, the outcome assessment would be conducted by someone other than the clinician who provided the intervention.

Factors associated with seasonal affective disorders in Lebanese adults

PURPOSE

Evaluate seasonal affective disorder (SAD) and the possible factors associated with it among Lebanese adults, during winter and summer seasons of 2018 and 2019.

DESIGN AND METHODS

Cross-sectional study was conducted in two time intervals. The winter period took place from December 2018 to January 2019, whereas the summer period was from May to June 2019, evaluating the same participants.

FINDINGS

Higher winter depression (adjusted odds ratio [ORa] = 1.16), higher winter insomnia (ORa = 1.04) were significantly associated with higher odds of having winter SAD. Higher summer depression (ORa = 1.20) was significantly associated with higher odds of having summer SAD.

PRACTICE IMPLICATIONS

A proper recognition of risk factors associated with SAD allows the clinician to effectively differentiate between SAD and nonseasonal depressive symptoms.

Predictability of Seasonal Mood Fluctuations Based on Self-Report Questionnaires and EEG Biomarkers in a Non-clinical Sample

Induced by decreasing light, people affected by seasonal mood fluctuations may suffer from low energy, have low interest in activities, experience changes in weight, insomnia, difficulties in concentration, depression, and suicidal thoughts. Few studies have been conducted in search for biological predictors of seasonal mood fluctuations in the brain, such as EEG oscillations. A sample of 64 participants was examined with questionnaires and electroencephalography in summer. In winter, a follow-up survey was recorded and participants were grouped into those with at least mild (N = 18) and at least moderate (N = 11) mood decline and those without self-reported depressive symptoms both in summer and in winter (N = 46). A support vector machine was trained to predict mood decline by either EEG biomarkers alone, questionnaire data from baseline alone, or a combination of the two. Leave-one-out-cross validation with lasso regularization was used with logistic regression to fit a model. The accuracy for classification for at least mild/moderate mood decline was 77/82% for questionnaire data, 72/82% for EEG alone, and 81/86% for EEG combined with questionnaire data. Self-report data was more conclusive than EEG biomarkers recorded in summer for prediction of worsening of depressive symptoms in winter but it is advantageous to combine EEG with psychological assessment to boost predictive performance.

Detecting Critical Decision Points during Cognitive-Behavioral Therapy and Light Therapy for Winter Depression Nonremission and Recurrence

Using data from a clinical trial comparing cognitive-behavioral therapy (CBT-SAD) and light therapy (LT) for winter seasonal affective disorder (SAD; N = 177), we explored critical decision points, or treatment weeks, that predict likelihood of nonremission at post-treatment and depression recurrence following treatment. In receiver operator characteristic (ROC) curve analyses, we used weekly Structured Clinical Interview for the Hamilton Rating Scale for Depression-SAD Version (SIGH-SAD) scores during treatment to predict nonremission at post-treatment (Week 6) and recurrence one winter later (Winter 1), two winters later (Winter 2), and any recurrence. Although several C-statistics of ≥ .70 were found, only Week 4 SIGH-SAD scores in CBT-SAD for nonremission had enough predictive ability to inform clinical decision-making (C-statistic = .80; sensitivity = .91; specificity = .68). Week 4 of CBT-SAD may be a critical time point to identify likely nonremitters who need tailoring of intervention, based on SIGH-SAD cutpoint score ≥ 13. This study illustrates how clinical trial data can inform detecting optimal decision points in treatment for identifying patients unlikely to remit, a critical first step to developing adaptive treatment strategies using decision rules to operationalize when and for whom treatment should change to maximize clinical benefit.