Mobile Phone-Based Mood Ratings Prospectively Predict Psychotherapy Attendance

- This study used text messaging to measure daily mood in patients with depression.
- Low mood the day prior to psychotherapy increased the risk of nonattendance.
- These findings can inform “just-in-time” interventions to improve attendance.

Psychotherapy nonattendance is a costly and pervasive problem. While prior research has identified stable patient-level predictors of attendance, far less is known about dynamic (i.e., time-varying) factors. Identifying dynamic predictors can clarify how clinical states relate to psychotherapy attendance and inform effective “just-in-time” interventions to promote attendance. The present study examines whether daily mood, as measured by responses to automated mobile phone-based text messages, prospectively predicts attendance in group cognitive-behavioral therapy (CBT) for depression.Fifty-six Spanish-speaking Latino patients with elevated depressive symptoms (46 women, mean age = 50.92 years, SD = 10.90 years), enrolled in a manualized program of group CBT, received daily automated mood-monitoring text messages. Patients' daily mood ratings, message response rate, and delay in responding were recorded.Patients' self-reported mood the day prior to a scheduled psychotherapy session significantly predicted attendance, even after controlling for patients' prior attendance history and age (OR = 1.33, 95% CI [1.04, 1.70], p = .02). Positive mood corresponded to a greater likelihood of attendance.Our results demonstrate the clinical utility of automated mood-monitoring text messages in predicting attendance. These results underscore the value of text messaging, and other mobile technologies, as adjuncts to psychotherapy. Future work should explore the use of such monitoring to guide interventions to increase attendance, and ultimately the efficacy of psychotherapy.