Seeing is Believing: Biofeedback as a Tool to Enhance Motivation for Cognitive Therapy

Biofeedback ◽  
2015 ◽  
Vol 43 (4) ◽  
pp. 168-172 ◽  
Author(s):  
Erik Peper ◽  
Sakiko Nemoto ◽  
I-Mei Lin ◽  
Richard Harvey

Cognitive behavior therapy (CBT) as applied by behavioral scientists includes strategies for changing negative cognitions that contribute to depression and anxiety. Biofeedback is a useful strategy to demonstrate to clients the mind (cognitive, psychological) to body (physiological) interaction. For example, a cognitive, psychological reaction to a stimuli results in a physiological effects as illustrated by changes in skin conductance or muscle tension. A case example is used to demonstrate an anticipatory psychophysiological response resulting in covert activity of the forearm as a client simply imagines playing the piano.

2020 ◽  
Author(s):  
Aarathi Venkatesan ◽  
Lily Rahimi ◽  
Manpreet Kaur ◽  
Christopher Mosunic

BACKGROUND Digital mental health interventions offer a scalable solution that reduces barriers to seeking care for clinical depression and anxiety. OBJECTIVE We aimed to examine the effectiveness of a 12-week therapist supported, app-based cognitive behavioral therapy program in improving symptoms of depression and anxiety within 9 months. METHODS A total of 323 participants with mild to moderately severe depression or anxiety were enrolled in a 12-week digital cognitive behavior therapy program. The analysis was restricted to participants who provided at least one follow-up assessment after baseline. As a result, 146 participants (45.2%) were included in the analysis—74 (50.7%) participants completed assessments at 3 months, 31 participants (21.2%) completed assessments at 6 months, and 21 participants (14.4%) completed assessments at 9 months. The program included structured lessons and tools (ie, exercises and practices) as well as one-on-one weekly video counseling sessions with a licensed therapist for 12 weeks and monthly check-in sessions for 1 year. The clinically validated Patient Health Questionnaire (PHQ-8) and Generalized Anxiety Disorder Scale (GAD-7) were used to assess depression and anxiety, respectively. Linear mixed-effects modeling was employed to examine changes in depression and anxiety over time. RESULTS We observed a significant positive effect of program time on improvement in depression (β=–0.12, <i>P</i>&lt;.001) and anxiety scores (β=–0.10, <i>P</i>&lt;.001). At the end of the 12-week intervention, we observed an average reduction of 3.76 points (95% CI –4.76 to –2.76) in PHQ-8 scores. Further reductions in depression were seen at program month 6 (4.75-point reduction, 95% CI –6.61 to –2.88) and program month 9 (6.42-point reduction, 95% CI –8.66 to –6.55, <i>P</i>&lt;.001). A similar pattern of improvement emerged for anxiety, with a 3.17-point reduction at the end of the 12-week intervention (95% CI –4.21 to –2.13). These improvements were maintained at program month 6 (4.87-point reduction, 95% CI –6.85 to –2.87) and program month 9 (5.19-point, 95% –6.85 to 4.81). In addition, greater program engagement during the first 12 weeks predicted a greater reduction in depression (β=–0.29, <i>P</i>&lt;.001) CONCLUSIONS The results suggest that digital interventions can support sustained and clinically meaningful improvements in depression and anxiety. Furthermore, it appears that strong initial digital mental health intervention engagement may facilitate this effect. However, the study was limited by postintervention participant attrition as well as the retrospective observational study design.


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