Novel digital methods for gathering intensive time series data in mental health research: scoping review of a rapidly evolving field

Abstract

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.

Accessibility and feasibility of experience sampling methods for mental health research with people with intellectual disability: Scoping of research and stakeholder views

“…Thus, findings regarding the acceptability and feasibility of experience sampling for this population have just started to emerge. Because the field of intensive longitudinal research has been rapidly evolving in the past decades (Schick et al, 2023), more experience sampling studies including people with intellectual disability may be expected.…”

“…Nowadays, experience sampling studies use digital data collection methods, such as smartphones (e.g., de Vries et al, 2021). Researchers have been using experience sampling methods for assessing mental health from the early 1990s (Myin-Germeys et al, 2009) and this field has been rapidly evolving during the last two decades (Schick et al, 2023). As summarised by recent reviews, various mental health phenomena have been captured using experience sampling, such as emotion regulation (Boemo et al, 2022), mood and anxiety (Hall et al, 2021), and general well-being (de Vries et al, 2021) in populations without intellectual disability.…”

State and trait cognitions differentially affect cyclicity of mood and cortisol in individuals with and without premenstrual dysphoric disorder.

“…In PMDD, microinterventions targeting momentary rumination and PMA, but also more specific PMDD symptoms such as irritability or lack of a sense of control immediately during the late-luteal-phase may hold the potential to directly address the cyclical dynamics in the interplay of cognitions and mood. Along these lines, subjective and sensor data assessed via AA may be suitable to deliver tailored brief and specific just-in-time adaptive interventions in moments with high levels of distress or in moments of changing internal (e.g., rumination) and contextual (e.g., cycle phase) states (cf., Nahum-Shani et al, 2018; Schick et al, 2023).…”

“…Fourth, state rumination, by focusing on its process character uncontrollability, as well as state and trait PMA were assessed unidimensionally, and future research could profit from including other facets of state rumination (e.g., content-related aspects; cf., Rosenkranz et al, 2020) and of mindfulness (e.g., nonjudgmental acceptance; cf., Blanke & Brose, 2017) in addition to PMA. Moreover, additional fine-grained assessments of contextual characteristics within cycle phases (i.e., current social or physical activities) by combining active (e.g., momentary self-reports) and passive data acquisition (e.g., mobile sensing) may advance our understanding of risk factors as well as of critical external and internal determinants (Schick et al, 2023) of mood and cortisol variability in PMDD. Lastly, whereas within-subject couplings with time-lagged analyses, as done in the present study, are considered a good starting point to uncover potential causal associations, there is need for additional within-subject experimental approaches in daily life (cf., Huffziger et al, 2013; Kuehner et al, 2023; Schmiedek & Neubauer, 2020).…”

What digital health technology types are used in mental health prevention and intervention? Review of systematic reviews for systematization of technologies

“…Table 1 summarizes the results of this review. Many studies mention technologies such as accelerometry, 10 , 11 , 13 , 22-28 actigraphy, 26 , 29 gyroscopy, 23 , 25 pedometry, 23-25 , 27 skin temperature, 24 , 25 GPS, 10-13 , 22-29 Wi-Fi, 11 , 13 , 23 , 25 Bluetooth, 10-12 , 23 , 25 , 29 microphone, 10 , 11 , 23 , 25 , 28 heart rate sensor, 11 , 24 , 25 , 29 light sensor, 10 , 11 , 13 , 23 , 25 , 26 screen activity, 10-13 , 22 , 23 , 25 call logs, 10-12 , 22 , 23 , 25-27 email/text/SMS logs, 10-12 , 22-27 app usage, 10 , 22 , 23 , 25 social media logs, 12 , 22 , 24 , 25 , 27-29 keystroke logs, 13 , 22 , 29 self-report questionnaires, ...…”