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.

Experience sampling method (ESM) studies have found an association between daily stress and paranoid symptoms, but it is uncertain whether these findings generalize to physiological indicators of stress. Moreover, the temporality of the association and its moderating factors require further research. Here, we investigate whether physiological and self-rated daily stress predict subsequent paranoid symptoms and analyze the role of emotion regulation as a putative moderator. We applied ESM during 24 h to repeatedly assess heart rate, self-rated stress, and subclinical paranoia in a sample of 67 psychosis-prone individuals as measured with Community Assessment for Psychotic Experiences (CAPE). Adaptive and maladaptive emotion regulation was assessed at baseline with the Emotion Regulation Skills Questionnaire (ERSQ-ES) and the Cognitive Emotion Regulation Questionnaire (CERQ). Linear mixed models were used to analyze the data. Heart rate (b = 0.004, p < 0.05) and self-rated stress (b = 0.238, p < 0.001) predicted subsequent paranoia. The reverse effect, paranoia as a predictor of subsequent heart rate (b = 0.230, p = 0.615) or self-rated stress (b = –0.009, p = 0.751) was non-significant. Maladaptive emotion regulation was a significant predictor of paranoia (b = 0.740, p < 0.01) and moderated the path from self-rated stress to paranoia (b = 0.188, p < 0.05) but not the path from heart rate to paranoia (b = 0.005, p = 0.09). Our findings suggest a one-way temporal link between daily stress and paranoia and highlight the importance of emotion regulation as a vulnerability factor relevant to this process.

In the light of the recent publication of the DSM-5, there is renewed debate about the relative merit of categorical diagnosis, as laid down in Diagnostic and Statistical Manual of Mental Disorders (DSM) and International Classification of Diseases (ICD) diagnostic manuals. Issues such as validity, usefulness and acceptability of the diagnoses in this manual are increasingly debated. Several alternative possibilities have been suggested including: (i) the introduction of truly cross-cutting dimensional measures, that would facilitate dynamic multidimensional formulations of psychopathology, (ii) the Research Domain Criteria, that may facilitate biological research but move away from clinical symptoms, (iii) a system of personalized diagnosis based on psychopathology as a network of symptoms and contexts, and (iv) enhanced focus on motor alterations, other than catatonia, as a possible additional informative dimension of diagnosis in psychiatry, particularly as a possible marker of underlying neurodevelopmental alterations. We suggest that novel systems of diagnosis are likely to rely more on continuous monitoring of diagnostically relevant information in daily life, complementing retrospective symptom criteria in DSM and ICD. Patients and their families are likely to benefit from these projects, as novel models of diagnosis based on daily life information may be linked more strongly to treatment needs and prognosis.