Assessing affect in adolescents with e-diaries: multilevel confirmatory factor analyses of different factor models

In the last two decades, e-diary studies have gained increasing interest, with a dominant focus on mood and affect. Although requested in current guidelines, psychometric properties are rarely reported, and methodological investigations of factor structure, model fit, and the reliability of mood and affect assessment are limited. We used a seven-day e-diary dataset of 189 adolescent participants (12-17  years). The e-diary affect assessments revealed a considerable portion of within-person variance. The six-factor model showed the best model fit compared to the less complex models. Factor loadings also improved with the complexity of the models. Accordingly, we recommend that future e-diary studies of adolescents use the six-factor model of affect as well as reporting psychometric properties and model fit. For future e-diary scale development, we recommend using a minimum of three items per scale to enable the use of confirmatory multilevel factor analyses.

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

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.

Predicting Delay in Goal-Directed Action: An Experience Sampling Approach Uncovering Within-Person Determinants Involved in the Onset of Academic Procrastination Behavior

Academic procrastination involves the delayed implementation of actions required to fulfill study-related tasks. These behavioral delays are thought to result from momentary failures in self-regulation (i.e., within-person processes). Most previous studies focused on the role of trait-based individual differences in students' procrastination tendencies. Little is known about the within-person processes involved in the occurrence of procrastination behavior in real-life academic situations. The present study applied an event-based experience sampling approach to investigate whether the onset of task-specific delay behavior can be attributed to unfavorable changes in students' momentary appraisals of tasks (value, aversiveness, effort, expectations of success), which may indicate failures in self-regulation arise between critical phases of goal-directed action. University students (N = 75) used an electronic diary over eight days to indicate their next days' intentions to work on academic tasks and their task-specific appraisals (n = 582 academic tasks planned). For each task, a second query requested the next day determined whether students' task-related appraisals changed and whether they implemented their intention on time or delayed working on the respective task (n = 501 completed task-specific measurements). Students' general procrastination tendency was assessed at baseline using two established self-report questionnaires. Stepwise two-level logistic regression analyses revealed that within-person changes in task-related appraisals that reflected a devaluation of the study-related tasks increased the risk for an actual delay. The risk to delay decreased when students maintained a positive attitude toward the task. Students' general procrastination tendency did not predict individual differences in their task-specific delay behavior. We discuss these findings in light of the growing effort to understand the within-person processes that contribute to induce procrastination behavior under real-life academic conditions and illustrate how this knowledge can benefit the design of tasks and instructions that support students' self-regulation to their best.

Ambulatory Assessment for Physical Activity Research: State of the Science, Best Practices and Future Directions

Technological and digital progress benefits physical activity (PA) research. Here we compiled expert knowledge on how Ambulatory Assessment (AA) is utilized to advance PA research, i.e., we present results of the 2nd International CAPA Workshop 2019 "Physical Activity Assessment - State of the Science, Best Practices, Future Directions" where invited researchers with experience in PA assessment, evaluation, technology and application participated. First, we provide readers with the state of the AA science, then we give best practice recommendations on how to measure PA via AA and shed light on methodological frontiers, and we furthermore discuss future directions. AA encompasses a class of methods that allows the study of PA and its behavioral, biological and physiological correlates as they unfold in everyday life. AA includes monitoring of movement (e.g., via accelerometry), physiological function (e.g., via mobile electrocardiogram), contextual information (e.g., via geolocation-tracking), and ecological momentary assessment (EMA; e.g., electronic diaries) to capture self-reported information. The strengths of AA are data assessment that near realtime, which minimizes retrospective biases in real-world settings, consequentially enabling ecological valid findings. Importantly, AA enables multiple assessments across time within subjects resulting in intensive longitudinal data (ILD), which allows unraveling within-person determinants of PA in everyday life. In this paper, we show how AA methods such as triggered e-diaries and geolocation-tracking can be used to measure PA and its correlates, and furthermore how these findings may translate into real-life interventions. In sum, AA provides numerous possibilities for PA research, especially the opportunity to tackle within- subject antecedents, concomitants, and consequences of PA as they unfold in everyday life. In-depth insights on determinants of PA could help us design and deliver impactful interventions in real-world contexts, thus enabling us to solve critical health issues in the 21st century such as insufficient PA and high levels of sedentary behavior.