Determinants of Success for Computerized Cognitive Behavior Therapy: Examination of an Insomnia Program

Abstract

This study evaluated plausible moderators of outcome in a 6-week computerized treatment for insomnia. Using secondary data from two randomized controlled trials, participants were 228 adults with chronic insomnia. Participants received computerized treatment from their homes. Outcomes were assessed using a sleep diary, as well as several standardized self-report scales. Using linear mixed models with SPSS, treatment was largely robust to comorbid conditions, education, age, and gender. Results showed that psychiatric comorbidity and education moderated the impact of treatment on fatigue and that sleep symptom comorbidity moderated the impact of treatment on maladaptive attitudes about sleep. Implications of these findings are that more widespread use of computerized treatment for insomnia may be warranted.

Dissemination of an Internet-Based Treatment for Chronic Insomnia Into Primary Care

“…A series of studies suggests that return2sleep.com is effective in improving sleep quality, insomnia severity, and daytime fatigue (Vincent & Lewycky, 2009), and that these effects may be mediated by reductions in hyperarousal and potential conditioning associated with extended time awake in bed , as well as improvements in perceived control over sleep (Vincent, Walsh, & Lewycky, 2010). Further, return2sleep.com has been found to be effective across comorbid psychiatric and sleep disorder conditions, education, age, and gender (Vincent, Walsh, & Lewycky, 2013).…”

Stepped Care for Insomnia: An Evaluation of Implementation in Routine Practice

“…Support for the validity of the method was previously documented. 30 With established reliability and validity, the Mini-International Neuropsychiatric Interview, 31 a structured clinical interview for the Diagnostic and Statistical Manual for Mental Disorders axis I disorders, was administered by the study coordinator and used to assess psychiatric comorbidity. We also administered the Clinical Global Improvement Scale (CGI) at the end of step 1 to measure perceptions of change in sleep.…”

Digital Cognitive Behavioral Therapy (dCBT) for Insomnia: a State-of-the-Science Review

“…If we focus on the predictive value of baseline sleep characteristics, it seems that comorbid sleep disorders [69], higher sleep efficiency [70], lower insomnia severity, and longer total sleep time predict less successful treatment or non-completion [71]. In addition, multiple studies reported psychiatric comorbidities to influence the effects of dCBT negatively [69, 71]. It has been suggested that people with high levels of depressive symptoms benefit more from support, whereas people with low levels of depressive symptoms improve regardless of support [40].…”

“…Regarding demographic predictors, a younger age and higher education have been reported as a positive predictor for improvement with dCBT [69, 70], but a previously published conference abstract suggested that only 2.2% of the variance in sleep efficiency increase was explained by demographics [70]. Since dCBT, like CBT given in any other modality, does not seem to be effective for everyone, the prediction of responders is a particularly important question.…”