A therapeutic application of the experience sampling method in the treatment of depression: a randomized controlled trial

Abstract

In depression, the ability to experience daily life positive affect predicts recovery and reduces relapse rates. Interventions based on the experience sampling method (ESM-I) are ideally suited to provide insight in personal, contextualized patterns of positive affect. The aim of this study was to examine whether add-on ESM-derived feedback on personalized patterns of positive affect is feasible and useful to patients, and results in a reduction of depressive symptomatology. Depressed outpatients (n5102) receiving pharmacological treatment participated in a randomized controlled trial with three arms: an experimental group receiving add-on ESM-derived feedback, a pseudo-experimental group participating in ESM but receiving no feedback, and a control group. The experimental group participated in an ESM procedure (three days per week over a 6-week period) using a palmtop. This group received weekly standardized feedback on personalized patterns of positive affect. Hamilton Depression Rating Scale -17 (HDRS) and Inventory of Depressive Symptoms (IDS) scores were obtained before and after the intervention. During a 6-month follow-up period, five HDRS and IDS assessments were completed. Add-on ESM-derived feedback resulted in a significant and clinically relevant stronger decrease in HDRS score relative to the control group (p<0.01; 25.5 point reduction in HDRS at 6 months). Compared to the pseudo-experimental group, a clinically relevant decrease in HDRS score was apparent at 6 months (B523.6, p50.053). Self-reported depressive complaints (IDS) yielded the same pattern over time. The use of ESM-I was deemed acceptable and the provided feedback easy to understand. Patients attempted to apply suggestions from ESM-derived feedback to daily life. These data suggest that the efficacy of traditional passive pharmacological approach to treatment of major depression can be enhanced by using person-tailored daily life information regarding positive affect.

Clinical Network Analysis in a Bipolar Patient Using an Experience Sampling Mobile Health Tool: An n=1 Study

“…This theory is in agreement with the current finding that it is not the presence and the central place in the network of depressive mood, but rather the absence or non-central role of positive affect that marks the difference between the depressive and hypomanic period. Insight in ESM-based mood networks may aid treatment, as previous research showed that ESM-derived feedback on positive affect results in a lasting decrease in depressive symptomatology [20].…”

“…Feedback using individual ESM data focussing on positive affect has been associated with decreases in depressive symptoms [20] and, thus, may serve to neutralise negative affective states. Based on the present results and results from a previous n=1 study [8], we may conclude that the prolonged use of ESM can help in treatment.…”

Automatic detection of emotions in depressed patients and healthy subjects: a mobile application study (Preprint)

“…Accordingly, Kramer et al provided feedback to momentary positive affect reported by depressed patients, which led to an improvement of depressive symptoms [27]. An emotion-aware mobile system has the potential to bypass the requirement of subjective reporting and directly provide intervention.…”

Jeeves - A visual programming environment for mobile experience sampling

“…Traditionally, ESM studies have been conducted using an external signalling device (such as a pager or pre-programmed electronic watch) coupled with paperbased forms for participants to fill out when the device emits a signal [2]. This cumbersome and unreliable approach has been greatly improved upon in recent years, where researchers have recognised the potential of mobile applications for conducting these types of studies with participants [3], [4], [5]. Along with this, in the field of Human Computer Interaction (HCI) study applications have been developed which allow both the signalling and data collection to take place on a participant's personal device.…”

“…The researchers behind EmotionSense have provided open-source libraries for facilitating the creation of ESM applications, but utilising these libraries still requires Android programming experience. Instead, non-programmers must invest in commercial software or hardware [3], [4], [5], hire professional developers [8], employ manual methods such as telephoning participants [9] or simply resort to providing paper-based logs [2], [10].…”