In the mental health field, there is a growing awareness that the study of psychiatric symptoms in the context of everyday life, using experience sampling methodology (ESM), may provide a powerful and necessary addition to more conventional research approaches. ESM, a structured self-report diary technique, allows the investigation of experiences within, and in interaction with, the real-world context. This paper provides an overview of how zooming in on the micro-level of experience and behaviour using ESM adds new insights and additional perspectives to standard approaches. More specifically, it discusses how ESM: a) contributes to a deeper understanding of psychopathological phenomena, b) allows to capture variability over time, c) aids in identifying internal and situational determinants of variability in symptomatology, and d) enables a thorough investigation of the interaction between the person and his/her environment and of real-life social interactions. Next to improving assessment of psychopathology and its underlying mechanisms, ESM contributes to advancing and changing clinical practice by allowing a more fine-grained evaluation of treatment effects as well as by providing the opportunity for extending treatment beyond the clinical setting into real life with the development of ecological momentary interventions. Furthermore, this paper provides an overview of the technical details of setting up an ESM study in terms of design, questionnaire development and statistical approaches. Overall, although a number of considerations and challenges remain, ESM offers one of the best opportunities for personalized medicine in psychiatry, from both a research and a clinical perspective.

We proposed an optimal deep learning architecture and its parameters for stress recognition, and the theoretical consideration on how to design the deep learning structure based on the periodic patterns of the raw ECG data. Experimental results in this study have proved that the proposed deep learning model, the Deep ECGNet, is an optimal structure to recognize the stress conditions using ultra short-term ECG data.

The current article describes the Acceptance and Commitment Therapy (ACT) in Daily Life (ACT-DL) training, a new mobile health treatment protocol for ACT applied in a randomized controlled trial in early psychosis individuals. Between weekly ACT therapy sessions, patients fill out brief questionnaires on an app about their mood, symptoms, activity, and current context, thus promoting awareness-a crucial component of ACT. The app also provides them with visual cues and exercises specifically related to the ACT sessions, to help them implement the techniques previously learned in therapy into their daily lives. Here we assess the feasibility of this protocol in 16 early psychosis individuals, as part of an ongoing randomized controlled trial. Specifically, we investigate the experienced usefulness of the ACT therapy and app, and burden of the protocol. Results indicate that participants find both the therapy sessions and the app useful, and that ACT-DL guides them in putting ACT into everyday practice, although the protocol may be moderately burdensome. These findings indicate that ACT-DL may help early psychosis patients applying ACT skills to diverse contexts of everyday life. Since ACT is not symptom-specific, ACT-DL may also be suited for different target populations. Limitations and future directions are discussed.

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