Insomnia and objectively measured sleep disturbances predict treatment outcome in depressed patients treated with psychotherapy or psychotherapy-pharmacotherapy combinations

Abstract

Objective: Insomnia and objectively measured sleep disturbances predict poor treatment outcomes in patients with major depressive disorder (MDD). However, prior research has utilized individual clinical trials with relatively small sample sizes and has focused on insomnia symptoms or objective measures, but not both. The present study is a secondary analysis that examines the degree to which insomnia, objective sleep disturbances, or their combination predicts depression remission following pharmacotherapy and/or psychotherapy treatment.

Method: Participants were 711 depressed (DSM criteria) patients drawn from 6 clinical trials. Remission status, defined as a score of ≤ 7 on the Hamilton Depression Rating Scale (HDRS) over 2 consecutive months, served as the primary outcome. Insomnia was assessed via the 3 sleep items on the HDRS. Objectively measured short sleep duration (total sleep time ≤ 6 hours) and prolonged sleep latency (> 30 minutes) or wakefulness after sleep onset (> 30 minutes) were derived from in-laboratory polysomnographic sleep studies. Logistic regression predicted the odds of nonremission according to insomnia, each of the objective sleep disturbances, or their combination, after adjusting for age, sex, treatment modality, and baseline depressive symptoms.

Results: Prolonged sleep latency alone (OR = 3.53; 95% CI, 1.28-9.73) or in combination with insomnia (OR = 2.11; 95% CI, 1.13-3.95) predicted increased risk of nonremission. In addition, insomnia and sleep duration individually and in combination were each associated with a significantly increased risk of nonremission (P values < .05).

Conclusions: Findings suggest that objectively measured prolonged sleep latency and short sleep duration independently or in conjunction with insomnia are risk factors for poor depression treatment outcome.

Figure 1. Logistic regression model predicting the odds of non-remission according to insomnia or individual objective sleep disturbances

Filled diamonds depict adjusted odds ratios predicting non-remission; filled circles depict lower and upper 95% confidence limits. Odds ratios are adjusted for age, sex, antidepressant medication usage (yes/no), duration of follow-up, and baseline depressive symptom severity (HRSD with sleep items removed). SL = sleep latency; WASO = wakefulness after sleep onset; TST = total sleep time.

Figure 2

Distribution of sleep subgroups according to insomnia and objective Sleep Criterion (TST < 6 hours or SL > 30 minutes, respectively).

Figure 3. Logistic regression model predicting the odds of non-remission according to the presence or absence of insomnia and objective sleep disturbances

Filled diamonds depict adjusted odds ratios predicting non-remission; filled circles depict lower and upper 95% confidence limits. Odds ratios are adjusted for age, sex, antidepressant medication usage (yes/no), duration of follow-up, and baseline depressive symptom severity (HRSD with sleep items removed). SL = sleep latency; WASO = wakefulness after sleep onset; TST = total sleep time.

Figure 4. Logistic regression model predicting the odds of non-remission according to increasing numbers of sleep disturbances

Filled diamonds depict adjusted odds ratios predicting non-remission; filled circles depict lower and upper 95% confidence limits. Odds ratios are adjusted for age, sex, antidepressant medication usage (yes/no), duration of follow-up, and baseline depressive symptom severity (HRSD with sleep items removed). Referent group are those with no sleep disturbances.