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Background: The frequency and clinical impact of Sudden Gains—large symptom improvements during a single between-session interval—in psychotherapy for depression have been well established. However, there have been relatively few efforts to identify the processes that lead to sudden gains.Aim: To explore therapy processes associated with sudden gains in cognitive therapy for depression by examining changes in the sessions surrounding the gains, and the session preceding the gain in particular.Methods: Using ratings of video-recordings (n = 36), we assessed the content, frequency and magnitude of within-session cognitive-, behavioral-, and interpersonal change, as well as the quality of the therapeutic alliance in the session prior to the gain (pre-gain session), the session after the gain (post-gain session) and a control session. After that, we contrasted scores in the pre-gain session with those in the control session. In addition, we examined changes that occurred between the pre- and post-gain session (between-session changes) and explored patients' attributions of change.Results: Although not statistically significant, within-session changes were more frequent and stronger in the pre-gain session compared to the control session. The largest difference between the pre-gain and control session was found in the behavioral domain, and reached the level of trend-significance. There were more, and more impactful between-session changes in the interval during which the gain occurred as compared to a control interval. Exploratory analysis of attributions of change revealed eight subcategories, all corresponding with the cognitive-, behavioral- and interpersonal- domain. The quality of the therapeutic alliance was high and almost identical in all sessions.Conclusion: In spite of its small sample size, our study provides relevant descriptive information about potential precipitants of, themes related to, and attributions given for sudden gains. Furthermore, our study provides clear suggestions for future research. A better understanding of session content in the sessions surrounding sudden gains may provide insight into the mechanisms of change in psychotherapy, hereby suggesting treatment-enhancing strategies. We encourage researchers to conduct research that could clarify the nature of these mechanisms, and believe the methods used in this study could serve as a framework for further work in this area.

Zusammenfassung Hintergrund Die stationsäquivalente psychiatrische Behandlung (StäB) wurde 2018 als Krankenhausleistung für Menschen eingeführt, die die Kriterien einer stationären Behandlung erfüllen. Die rasanten Fortschritte im Bereich der Informations- und Kommunikationstechnologie bieten neue Chancen für innovative digitale Versorgungsangebote wie telemedizinische, eHealth- oder mHealth-Verfahren. Ziel der Arbeit Diese Übersichtsarbeit soll einen umfassenden Überblick über neue digitale Versorgungsformen geben, die zur Personalisierung der StäB bei schweren psychischen Erkrankungen beitragen und somit klinische und soziale Outcomes verbessern sowie direkte und indirekte Kosten reduzieren könnten. Methode Diese Arbeit basiert auf einer selektiven Literaturrecherche (Narratives Review). Ergebnisse Es wurden vier primäre digitale Versorgungsformen identifiziert, die in der StäB gewinnbringend genutzt werden könnten: (1) Kommunikation, Behandlungskontinuität und -flexibilität durch Online-Chat und Videotelefonie, (2) Monitoring von Symptomen und Verhaltensweisen in Echtzeit durch Anwendung des ambulatorischen Assessments („ecological momentary assessment“ [EMA]), (3) Nutzung multimodaler EMA-Daten für die Generierung von personalisiertem Feedback über subjektives Erleben und Verhaltensmuster sowie (4) auf Person, Moment und Kontext zugeschnittene, adaptive ambulatorische Interventionen („ecological momentary interventions“ [EMIs]). Diskussion Digitale Versorgungsformen haben erhebliches Potenzial die Effektivität und Kosteneffektivität der StäB zu steigern. Ein wichtiger nächster Schritt besteht darin, die Anwendung dieser Versorgungsformen im Bereich der StäB zu modellieren und deren Qualität aus Sicht der Patient*innen, Sicherheit und initiale Prozess- und Ergebnisqualität sowie Implementierungsbedingungen sorgfältig zu untersuchen.

Recent technological advances enable the collection of intensive longitudinal data. This scoping review aimed to provide an overview of methods for collecting intensive time series data in mental health research as well as basic principles, current applications, target constructs, and statistical methods for this type of data. In January 2021, the database MEDLINE was searched. Original articles were identified that (1) used active or passive data collection methods to gather intensive longitudinal data in daily life, (2) had a minimum sample size of N ⩾ 100 participants, and (3) included individuals with subclinical or clinical mental health problems. In total, 3799 original articles were identified, of which 174 met inclusion criteria. The most widely used methods were diary techniques (e.g. Experience Sampling Methodology), various types of sensors (e.g. accelerometer), and app usage data. Target constructs included affect, various symptom domains, cognitive processes, sleep, dysfunctional behaviour, physical activity, and social media use. There was strong evidence on feasibility of, and high compliance with, active and passive data collection methods in diverse clinical settings and groups. Study designs, sampling schedules, and measures varied considerably across studies, limiting the generalisability of findings. Gathering intensive longitudinal data has significant potential to advance mental health research. However, more methodological research is required to establish and meet critical quality standards in this rapidly evolving field. Advanced approaches such as digital phenotyping, ecological momentary interventions, and machine-learning methods will be required to efficiently use intensive longitudinal data and deliver personalised digital interventions and services for improving public mental health.