Recovering Within-Person Dynamics from Psychological Time Series

Daily sampling frequency and sampling duration affect reliability of person-level estimates of physical activity outcomes: Optimizing Ecological Momentary Assessment studies of physical activities

Studies on the interrelationship between physical activity (PA) behaviors and EMA-assessed constructs should use measures with high reliability of both the EMA-assessed constructs and the time-matched accelerometry-assessed PA behavior. The aim of this paper is to evaluate how the reliability of accelerometry-assessed PA outcomes is affected by different EMA sampling schemes. Emulating relevant sampling schemes in EMA studies, multiple random samples of real-world accelerometer data (measured via activPAL worn for ∼7 days) were drawn that varied in the number of daily samples (3, 5, and 7 daily samples) and in the duration of each sample (5 min, 60 min, and 120 min), totaling 9 sampling schemes. The reliability of the resulting PA outcomes was estimated by correlating weekly aggregates of the sampled data with the true parameter values (weekly aggregates of all data). A total of 4231 days were analyzed from 619 participants (mean age 45 ± 16 years; 73.3 % female; 72.7 % Non-Hispanic White). At the lowest sampling duration (5 min), none of the sampling schemes reached the threshold for acceptable reliability (reliabilities ranged from 0.20 to 0.67). Using 120 min sample duration resulted in reliable person-level PA estimates (ranges 0.77 to 0.97) except for the time spent in sedentary behavior (rel = 0.68), and for minimum (rel = 0.66), and maximum (rel = 0.63) PA intensity. These three PA outcomes consistently yielded lower levels of reliability across all sampling schemes. Holding constant the total time covered in a day, sampling schemes that used more frequent samples with shorter duration resulted in greater reliability compared to sampling schemes that used less frequent samples with longer duration. Overall, the results are consistent with sampling theory - reliability increased as more time was covered. However, our results also suggested interactive effects of reliability based on sampling frequency, duration, and PA outcome. Researchers should be aware that the reliability of accelerometer-derived PA outcomes can vary depending on the sampling design of EMA studies.

Time-Varying Network Models for the Temporal Dynamics of Depressive Symptomatology in Patients With Depressive Disorders: Secondary Analysis of Longitudinal Observational Data

BACKGROUND

As depression is highly heterogenous, an increasing number of studies investigate person-specific associations of depressive symptoms in longitudinal data. However, most studies in this area of research conceptualize symptom interrelations to be static and time invariant, which may lead to important temporal features of the disorder being missed.

OBJECTIVE

To reveal the dynamic nature of depression, we aimed to use a recently developed technique to investigate whether and how associations among depressive symptoms change over time.

METHODS

Using daily data (mean length 274, SD 82 d) of 20 participants with depression, we modeled idiographic associations among depressive symptoms, rumination, sleep, and quantity and quality of social contacts as dynamic networks using time-varying vector autoregressive models.

RESULTS

The resulting models showed marked interindividual and intraindividual differences. For some participants, associations among variables changed in the span of some weeks, whereas they stayed stable over months for others. Our results further indicated nonstationarity in all participants.

CONCLUSIONS

Idiographic symptom networks can provide insights into the temporal course of mental disorders and open new avenues of research for the study of the development and stability of psychopathological processes.

The future is dynamic: A call for intensive longitudinal data in immunopsychiatry

The long-term value of immunopsychiatry will be based on its ability to translate basic science into effective clinical interventions. In this article, we discuss a key obstacle to achieving this important translational goal-namely, the preponderance of studies that are cross-sectional, or that have months-to-years long follow-up periods. Immunopsychiatric processes such as stress, inflammation, and depression symptoms are inherently dynamic and fluctuate over hours, days, and weeks. This fact suggests that higher-density data collection with only days between measurements is necessary to capture-with adequate resolution-the actual dynamics of these systems, determine optimal time lags with which to observe associations between variables of interest, and maximize the translational potential of these data. To illustrate these points, we use pilot data from our own intensive longitudinal immunopsychiatric study. We then conclude by making several recommendations for future research. By learning how to better use existing data for dynamically informative studies as well as collecting intensive longitudinal data, we believe immunopsychiatry will be much better positioned to advance our causal understanding of the interplay between the immune system and health.

These Are Not the Effects You Are Looking for: Causality and the Within-/Between-Persons Distinction in Longitudinal Data Analysis

In psychological science, researchers often pay particular attention to the distinction between within- and between-persons relationships in longitudinal data analysis. Here, we aim to clarify the relationship between the within- and between-persons distinction and causal inference and show that the distinction is informative but does not play a decisive role in causal inference. Our main points are threefold. First, within-persons data are not necessary for causal inference; for example, between-persons experiments can inform about (average) causal effects. Second, within-persons data are not sufficient for causal inference; for example, time-varying confounders can lead to spurious within-persons associations. Finally, despite not being sufficient, within-persons data can be tremendously helpful for causal inference. We provide pointers to help readers navigate the more technical literature on longitudinal models and conclude with a call for more conceptual clarity: Instead of letting statistical models dictate which substantive questions researchers ask, researchers should start with well-defined theoretical estimands, which in turn determine both study design and data analysis.

Some Recommendations on the Use of Daily Life Methods in Affective Science

Real-world emotions are often more vivid, personally meaningful, and consequential than those evoked in the lab. Therefore, studying emotions in daily life is essential to test theories, discover new phenomena, and understand healthy emotional functioning; in short, to move affective science forward. The past decades have seen a surge of research using daily diary, experience sampling, or ecological momentary assessment methods to study emotional phenomena in daily life. In this paper, we will share some of the insights we have gained from our collective experience applying such daily life methods to study everyday affective processes. We highlight what we see as important considerations and caveats involved in using these methods and formulate recommendations to improve their use in future research. These insights focus on the importance of (i) theory and hypothesis-testing; (ii) measurement; (iii) timescale; and (iv) context, when studying emotions in their natural habitat.

Anticipating manic and depressive transitions in patients with bipolar disorder using early warning signals

Background

In bipolar disorder treatment, accurate episode prediction is paramount but remains difficult. A novel idiographic approach to prediction is to monitor generic early warning signals (EWS), which may manifest in symptom dynamics. EWS could thus form personalized alerts in clinical care. The present study investigated whether EWS can anticipate manic and depressive transitions in individual patients with bipolar disorder.

Methods

Twenty bipolar type I/II patients (with ≥ 2 episodes in the previous year) participated in ecological momentary assessment (EMA), completing five questionnaires a day for four months (Mean = 491 observations per person). Transitions were determined by weekly completed questionnaires on depressive (Quick Inventory for Depressive Symptomatology Self-Report) and manic (Altman Self-Rating Mania Scale) symptoms. EWS (rises in autocorrelation at lag-1 and standard deviation) were calculated in moving windows over 17 affective and symptomatic EMA states. Positive and negative predictive values were calculated to determine clinical utility.

Results

Eleven patients reported 1–2 transitions. The presence of EWS increased the probability of impending depressive and manic transitions from 32-36% to 46–48% (autocorrelation) and 29–41% (standard deviation). However, the absence of EWS could not be taken as a sign that no transition would occur in the near future. The momentary states that indicated nearby transitions most accurately (predictive values: 65–100%) were full of ideas, worry, and agitation. Large individual differences in the utility of EWS were found.

Conclusions

EWS show theoretical promise in anticipating manic and depressive transitions in bipolar disorder, but the level of false positives and negatives, as well as the heterogeneity within and between individuals and preprocessing methods currently limit clinical utility.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40345-022-00258-4.

Just-in-Time Adaptive Interventions for Suicide Prevention: Promise, Challenges, and Future Directions

The suicide rate (currently 14 per 100,000) has barely changed in the United States over the past 100 years. There is a need for new ways of preventing suicide. Further, research has revealed that suicidal thoughts and behaviors and the factors that drive them are dynamic, heterogeneous, and interactive. Most existing interventions for suicidal thoughts and behaviors are infrequent, not accessible when most needed, and not systematically tailored to the person using their own data (e.g., from their own smartphone). Advances in technology offer an opportunity to develop new interventions that may better match the dynamic, heterogeneous, and interactive nature of suicidal thoughts and behaviors. Just-In-Time Adaptive Interventions (JITAIs), which use smartphones and wearables, are designed to provide the right type of support at the right time by adapting to changes in internal states and external contexts, offering a promising pathway toward more effective suicide prevention. In this review, we highlight the potential of JITAIs for suicide prevention, challenges ahead (e.g., measurement, ethics), and possible solutions to these challenges.

On the transience or stability of subthreshold psychopathology

Symptoms of psychopathology lie on a continuum ranging from mental health to psychiatric disorders. Although much research has focused on progression along this continuum, for most individuals, subthreshold symptoms do not escalate into full-blown disorders. This study investigated how the stability of psychopathological symptoms (attractor strength) varies across severity levels (homebase). Data were retrieved from the TRAILS TRANS-ID study, where 122 at-risk young adults (mean age 23.6 years old, 57% males) monitored their mental states daily for a period of six months (± 183 observations per participant). We estimated each individual's homebase and attractor strength using generalized additive mixed models. Regression analyses showed no association between homebases and attractor strengths (linear model: B = 0.02, p = 0.47, R2 < 0.01; polynomial model: B < 0.01, p = 0.61, R2 < 0.01). Sensitivity analyses where we (1) weighed estimates according to their uncertainty and (2) removed individuals with a DSM-5 diagnosis from the analyses did not change this finding. This suggests that stability is similar across severity levels, implying that subthreshold psychopathology may resemble a stable state rather than a transient intermediate between mental health and psychiatric disorder. Our study thus provides additional support for a dimensional view on psychopathology, which implies that symptoms differ in degree rather than kind.

Within- and across-day patterns of interplay between depressive symptoms and related psychopathological processes: a dynamic network approach during the COVID-19 pandemic

BACKGROUND

In order to understand the intricate patterns of interplay connected to the formation and maintenance of depressive symptomatology, repeated measures investigations focusing on within-person relationships between psychopathological mechanisms and depressive components are required.

METHODS

This large-scale preregistered intensive longitudinal study conducted 68,240 observations of 1706 individuals in the general adult population across a 40-day period during the COVID-19 pandemic to identify the detrimental processes involved in depressive states. Daily responses were modeled using multi-level dynamic network analysis to investigate the temporal associations across days, in addition to contemporaneous relationships between depressive components within a daily window.

RESULTS

Among the investigated psychopathological mechanisms, helplessness predicted the strongest across-day influence on depressive symptoms, while emotion regulation difficulties displayed more proximal interactions with symptomatology. Helplessness was further involved in the amplification of other theorized psychopathological mechanisms including rumination, the latter of which to a greater extent was susceptible toward being influenced rather than temporally influencing other components of depressive states. Distinctive symptoms of depression behaved differently, with depressed mood and anhedonia most prone to being impacted, while lethargy and worthlessness were more strongly associated with outgoing activity in the network.

CONCLUSIONS

The main mechanism predicting the amplifications of detrimental symptomatology was helplessness. Lethargy and worthlessness revealed greater within-person carry-over effects across days, providing preliminary indications that these symptoms may be more strongly associated with pushing individuals toward prolonged depressive state experiences. The psychopathological processes of rumination, helplessness, and emotion regulation only exhibited interactions with the depressed mood and worthlessness component of depression, being unrelated to lethargy and anhedonia. The findings have implications for the impediment of depressive symptomatology during and beyond the pandemic period. They further outline the gaps in the literature concerning the identification of psychopathological processes intertwined with lethargy and anhedonia on the within-person level.

Measuring parent-adolescent interactions in natural habitats. The potential, status, and challenges of ecological momentary assessment

Few people are as important for an adolescent's development as their parents. However, most research on parent-adolescent relationships describes long-term population-wide effects. Therefore, little is known about everyday interactions between adolescents and parents in individual families. Ecological momentary assessment (EMA) measures families several times a day as they go through daily life. This approach provides ecologically valid insights into which interactions took place and how they were experienced. State-of-the-art EMA studies suggest that within-family fluctuations in parenting may trigger changes in an adolescent's well-being and behaviors. In practice, moreover, EMA may strengthen family support and intervention research. This article reviews recent empirical work, highlights the (un)used theoretical and practical promise of EMA and identifies key-challenges to unlock this full potential.