Intensive Longitudinal Data Research Articles

A Bayesian Approach to Modeling Variance of Intensive Longitudinal Biomarker Data as a Predictor of Health Outcomes.

Intensive longitudinal biomarker data are increasingly common in scientific studies that seek temporally granular understanding of the role of behavioral and physiological factors in relation to outcomes of interest. Intensive longitudinal biomarker data, such as those obtained from wearable devices, are often obtained at a high frequency typically resulting in several hundred to thousand observations per individual measured over minutes, hours, or days. Often in longitudinal studies, the primary focus is on relating the means of biomarker trajectories to an outcome, and the variances are treated as nuisance parameters, although they may also be informative for the outcomes. In this paper, we propose a Bayesian hierarchical model to jointly model a cross-sectional outcome and the intensive longitudinal biomarkers. To model the variability of biomarkers and deal with the high intensity of data, we develop subject-level cubic B-splines and allow the sharing of information across individuals for both the residual variability and the random effects variability. Then different levels of variability are extracted and incorporated into an outcome submodel for inferential and predictive purposes. We demonstrate the utility of the proposed model via an application involving bio-monitoring of hertz-level heart rate information from a study on social stress.

The Effect of Different Construct Operationalizations, Study Duration, and Preprocessing Choices on Power-Based Sample Size Recommendations in Intensive Longitudinal Research.

To shed light on the dynamics of psychological processes, researchers often collect intensive longitudinal (IL) data by asking people to repeatedly report on their momentary experiences in daily life. Two important decisions when designing an IL study concern the number of persons and the number of measurement occasions to be included. These sample size decisions are ideally based on statistical power considerations. When conducting statistical power analysis, it is necessary to provide the value of the effect size of interest as well as of all other model parameters. In IL research, these values have to be based on previous studies. This implies that these values are subject to large heterogeneity due to differences in study design and preprocessing choices. This between-study heterogeneity can severely impact power-based sample size recommendations. In this article, we introduce an approach to investigate the impact of study design and pre-processing of previous studies and to determine a recommended sample size to account for this impact. We demonstrate how to use this approach to investigate the effect of different construct operationalizations, study duration, and preprocessing choices. This approach paves the way for more thoughtful and robust sample-size decisions.

State gender variability and body satisfaction among sexual minority men

Prominent theories of gender suggest that gender can be variable, rather than static. For example, a person may experience changes in their masculinity and femininity in daily life, which we refer to as ‘state gender variability.’ Theory and research suggest that the degree to which masculinity and femininity fluctuate may have implications for body satisfaction. In this study, we analysed intensive longitudinal data to gain nuanced insights into how masculinity and femininity vary in everyday life among a sample of majority cis-gender sexual minority men. We first present a comprehensive descriptive analysis of gender variability. Second, we test whether individual differences in gender variability are associated with body satisfaction. Masculinity and femininity were moderately stable, with substantial within-person variability. Masculinity and femininity tended to be more variable than state body satisfaction and negative affect. Further, variability and instability in masculinity were associated with lower body satisfaction. Conversely, variability in femininity was associated with higher scores on body satisfaction. Our study contributes to a growing literature examining the implications of masculinity and femininity for sexual minority men’s body image and opens up new lines of inquiry focused on state gender variability.

Effect of engagement in personal growth initiative skills on depression, anxiety, and stress: Multilevel mediation analysis with intensive longitudinal data.

The current study examined the association between the possession of personal growth initiative (PGI) skills and symptoms of mental illness (i.e., depression, anxiety, and stress) and the potential mediating role of daily engagement of PGI skills during the COVID-19 pandemic. The participants consisted of 73 individuals (Mage = 41.62, SDage = 12.44) who were recruited from Amazon's Mechanical Turk (MTurk). Data for possessed PGI was collected in April 2020, and the daily survey started approximately 1 month later at the beginning of May 2020 for 29 days. Controlling for demographic variables, multilevel mediation models revealed significant association between PGI skills and PGI engagement. However, our hypothesized indirect effects were not found. When inspecting the direct effects controlling for the effect of PGI engagement and demographic variables, the paths between PGI skills and both depression and anxiety were negatively associated. When examining the effect of time, there were no significant changes during the period of 29 days in the levels of PGI skills among individuals. However, a significant fluctuation within individuals in how much participants have utilized PGI skills during the study period was found. The current study highlights the influence of PGI skills at both the between-individual as well as within-individual levels in predicting symptoms of mental illness, and it also uncovers the role of PGI skills by separating into general PGI skills and individual's daily engagement in utilizing the PGI skills in predicting symptoms of mental illness.

Application of Ecological momentary assessment (EMA) in assessing the relationship between affect and movement behaviors among people with mood disorders: a scoping review

ABSTRACT Ecological momentary assessment (EMA) enables the generation of intensive longitudinal data to examine dynamic relationships between variables. This study aims to describe the use of EMA in assessing dynamic associations between movement behaviors (physical activity, sedentary behavior, and sleep) and affective experiences among people with affective disorders. This scoping review searched peer-reviewed journal articles in eight electronic databases in both June 2022 and October 2023. Twenty-two studies were identified. Affective constructs were inconsistently implemented conceptually and operationally. Most studies (4/5) comparing compliance rates between mood-disordered participants and healthy controls found no significant differences, supporting EMA feasibility for individuals with affective disorders. Sleep quality was consistently linked to higher positive affect, lower negative affect, and mood enhancements. Physical activity (6/8 studies) was consistently associated with mood enhancements or improved positive affect, but not negative affect (2/3 studies). One study investigated affect and an indicator of sedentary behavior. Our review highlights EMA feasibility for investigating movement behaviors and affective experiences among people with affective disorders. Understanding these associations may contribute to informing clinical management of affective disorders and developing behavioral interventions such as just-in-time adaptive interventions. However, enhancing EMA methodology design and reporting is necessary to improve study reliability and validity.

Dynamic Structural Equation Modeling with Cycles

Cyclical phenomena are commonly observed in many areas of repeated measurements, especially with intensive longitudinal data. A typical example is circadian (24-hour) rhythm of physical measures such as blood pressure, heart rate, glucose level, and alertness. This paper focuses on positive affect, which is a common measure in psychological studies and for which circadian rhythm has been observed but not analyzed by modern statistical methods. The paper demonstrates that a large new analysis arsenal is available for analysis of cyclical features in intensive longitudinal data. This can help researchers extract more information from their data to get more valid estimates of coupled processes and to get new theoretical insights into circadian rhythms of mood. To assist in this effort, the analyses are based on general models with a rich set of features while still being accessible without an unduly steep learning curve. Scripts for the Mplus software are available for all the analyses presented.

Measuring Environmental and Behavioral Drivers of Chronic Diseases Using Smartphone-Based Digital Phenotyping: Intensive Longitudinal Observational mHealth Substudy Embedded in 2 Prospective Cohorts of Adults.

Previous studies investigating environmental and behavioral drivers of chronic disease have often had limited temporal and spatial data coverage. Smartphone-based digital phenotyping mitigates the limitations of these studies by using intensive data collection schemes that take advantage of the widespread use of smartphones while allowing for less burdensome data collection and longer follow-up periods. In addition, smartphone apps can be programmed to conduct daily or intraday surveys on health behaviors and psychological well-being. The aim of this study was to investigate the feasibility and scalability of embedding smartphone-based digital phenotyping in large epidemiological cohorts by examining participant adherence to a smartphone-based data collection protocol in 2 ongoing nationwide prospective cohort studies. Participants (N=2394) of the Beiwe Substudy of the Nurses' Health Study 3 and Growing Up Today Study were followed over 1 year. During this time, they completed questionnaires every 10 days delivered via the Beiwe smartphone app covering topics such as emotions, stress and enjoyment, physical activity, access to green spaces, pets, diet (vegetables, meats, beverages, nuts and dairy, and fruits), sleep, and sitting. These questionnaires aimed to measure participants' key health behaviors to combine them with objectively assessed high-resolution GPS and accelerometer data provided by participants during the same period. Between July 2021 and June 2023, we received 11.1 TB of GPS and accelerometer data from 2394 participants and 23,682 survey responses. The average follow-up time for each participant was 214 (SD 148) days. During this period, participants provided an average of 14.8 (SD 5.9) valid hours of GPS data and 13.2 (SD 4.8) valid hours of accelerometer data. Using a 10-hour cutoff, we found that 51.46% (1232/2394) and 53.23% (1274/2394) of participants had >50% of valid data collection days for GPS and accelerometer data, respectively. In addition, each participant submitted an average of 10 (SD 11) surveys during the same period, with a mean response rate of 36% across all surveys (SD 17%; median 41%). After initial processing of GPS and accelerometer data, we also found that participants spent an average of 14.6 (SD 7.5) hours per day at home and 1.6 (SD 1.6) hours per day on trips. We also recorded an average of 1046 (SD 1029) steps per day. In this study, smartphone-based digital phenotyping was used to collect intensive longitudinal data on lifestyle and behavioral factors in 2 well-established prospective cohorts. Our assessment of adherence to smartphone-based data collection protocols over 1 year suggests that adherence in our study was either higher or similar to most previous studies with shorter follow-up periods and smaller sample sizes. Our efforts resulted in a large dataset on health behaviors that can be linked to spatial datasets to examine environmental and behavioral drivers of chronic disease.

Subsyndromes and symptom clusters: Multilevel factor analysis of behavioral and psychological symptoms of dementia with intensive longitudinal data.

Behavioral and psychological symptoms in dementia (BPSD) are dynamic phenomena with a high amount of intraindividual variability. We applied a multilevel framework to identify subsyndromes (between-person factors) that represent clinically relevant profiles of BPSD and identify symptom clusters (within-person factors) that represent contextually driven daily symptom experiences. This study used an intensive longitudinal design in which 68 co-residing family caregivers to persons living with dementia were recruited to proxy report on their care recipient's daily symptom experiences of 23 different BPSD for eight consecutive days (n=443 diaries). A multilevel exploratory/confirmatory factor analysis was used to account for nested data and separate within-person variances from between-level factor estimates. Exploratory factor analysis identified a 4-between 3-within factor structure based on fit statistics and clinical interpretability. This study offers major methodological and conceptual advancements for management of BPSD within Alzheimer's disease and related dementias by introducing two related but distinct concepts of subsyndromes and symptom clusters. Because behavioral and psychological symptoms of dementia (BPSD) are dynamic temporal phenomenon, this introduces measurement error into aggregate group-level estimates when trying to create subsyndromes. We propose a multilevel analysis to provide a more valid and reliable estimation by separating out variance due to within-person daily fluctuations. Using a multilevel exploratory factor analysis with intensive longitudinal data, we identified distinct and meaningful groups of BPSD. The four factors at the between-person level represented subsyndromes that are based on how BPSD co-occurred among persons with Alzheimer's disease (AD). These subsyndromes are clinically relevant because they share features of established clinical phenomena and may have similar neurobiological etiologies. We also found three within-person factors representing distinct symptom clusters. They are based on how BPSD clustered together on a given day for an individual with AD and related dementias. These clusters may have shared environmental triggers.

Recovery at your fingertips: pilot study of an mHealth intervention for work-related stress among nursing students

BackgroundWork-related stress is detrimental to individual health and incurs substantial social costs. Interventions to tackle this problem are urgently needed, with mHealth solutions being a promising way of delivering accessible and standardized interventions on a wide scale. This study pilot tests a low-intensive mHealth intervention designed to mitigate the negative consequences of stress through promoting recovery strategies.MethodsNursing school students (N = 16) used the intervention for a month. Data were collected immediately before, immediately after, and one month after the end of the intervention. Additionally, intensive longitudinal data were collected daily during the time of the intervention. Primary outcome measures include recruitment and retention rates, engagement with and acceptability of the intervention, as well as evaluating the quality of measurement instruments.ResultsRecruitment and retention rates provide a benchmark that we need to invite 10–12 times the intended target sample size. Engagement and acceptability metrics are promising overall, showing key areas that need to be adapted to improve the intervention. Measurement quality is acceptable with instruments mostly functioning as intended.ConclusionResults show that the intervention and study protocol are feasible for conducting a randomized controlled trial given a few adjustments. The randomization algorithm needs to match the sample size in order to allocate evenly distributed experimental groups. Acceptability of the intervention may be improved through adapting the recommended recovery strategies. Some additional outcome measures are suggested to provide a more comprehensive picture of intervention effects.Trial registrationNCT06228495. Registered retrospectively 01/10/2024.

Trajectory of associative memory impairment during electroconvulsive therapy in depression

Memory impairment is a serious cognitive side effect of electroconvulsive therapy (ECT) in the treatment of major depressive episodes (MDEs) and has garnered widespread attention in clinical practice, but its underlying evolution pattern during the course of ECT remains rarely understood in detail. Associative memory (AM) is a core indicator that reflects memory impairment in ECT. This study aimed to identify the dynamic trajectory of AM impairment and explore associated predictive factors. 405 intensive longitudinal AM data from 81 patients with MDE were collected at the baseline, after the first, third, fifth, and eighth ECT using five sets of face-cued word memory paradigms. Changes in AM score over time were analyzed using a linear mixed effects model. Trajectory subgroups and predictive factors were investigated using growth mixture model and logistic regression. AM score during ECT were significantly lower than at baseline, with the lowest scores observed after the eighth ECT session. Two trajectories of rapid (N = 56, 69.14%) and slow (N = 25, 30.86%) AM impairment were differentiated. Older female with lower education level were significant predictors contributing to more rapid memory impairment for ECT. The evolving pattern of associative memory impairment during ECT appears to occur early and worsen with subsequent treatment. This study may provide the important evidence understanding of the number effect of ECT sessions on memory impairment and suggest individual factors for predicting ECT memory outcome.

Revisiting unstable disability and the fluctuations of frailty: a measurement burst approach.

It has been hypothesised that frailty is the root cause of clinically observed but rarely systematically measured unstable disability among older adults. In this study, we measure the extent of short-term disability fluctuations and estimate their association with frailty using intensive longitudinal data. Repeated measurements of disability were collected under a measurement burst design in the FRequent health Assessment In Later life (FRAIL70+) study. A total of 426 community-dwelling older adults (70+) in Austria were interviewed about difficulties with basic, instrumental and mobility-related activities of daily living biweekly up to a total of 14 times in two measurement bursts (2891 and 2192 observations). Baseline frailty was assessed with both physical frailty (FP) and the frailty index (FI). Disability fluctuations were measured with the intra-individual interquartile range (iIQR) and estimated with a two-step generalised mixed regression procedure. Fewer participants were frail at baseline according to FP (11%) than FI (32%). Frail study participants reported not only more severe disability but also had more short-term disability fluctuations (iIQR = 1.0-1.5) compared with their robust counterparts (iIQR = 0). Regression models indicated that baseline frailty was associated with 2-3 times larger short-term disability fluctuations, which were also more prevalent among women, and increased with age and disability severity. Compared with those who were robust, frail older adults were characterised by not only more severe but also more unstable disability. Short-term disability fluctuations are closely tied to disability severity. Future studies should assess both stressors that may cause disability fluctuations among frail older adults as well as their potential consequences to inform frailty-centred care.

Compliance and response consistency in a lengthy intensive longitudinal data protocol.

Research on real-world patterns of substance use increasingly involves intensive longitudinal data (ILD) collection, requiring long assessment windows. The present study extends limited prior research examining event- and person-level influences on compliance and response consistency by investigating how these behaviors are sustained over time in an ILD study of alcohol and cannabis co-use in college students. Participants (n = 316) completed two 28-day bursts of ILD comprising five daily surveys, which included a morning survey of prior-day drinking. We used linear mixed effects models in a multilevel interrupted time series framework to evaluate the associations of time and measurement burst with (a) noncompliance (count of missed surveys) and (b) response consistency (difference between same-day report of drinking and morning report of prior-day drinking). We observed that time was positively associated with noncompliance, with no discontinuity associated with measurement burst. The slope of time was more positive in the second burst. Neither time nor measurement burst were significantly associated with consistent reporting. However, survey nonresponse and consistency of responding appeared to be impacted by the same-day use of substances. Overall, compliance decreased while consistency was stable across the duration of a lengthy ILD protocol. Shorter assessment windows or adaptive prompting strategies may improve overall study compliance. Further work examining daily burden and context is needed to inform future ILD design. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Burden and Inattentive Responding in a 12-Month Intensive Longitudinal Study: Interview Study Among Young Adults.

Intensive longitudinal data (ILD) collection methods have gained popularity in social and behavioral research as a tool to better understand behavior and experiences over time with reduced recall bias. Engaging participants in these studies over multiple months and ensuring high data quality are crucial but challenging due to the potential burden of repeated measurements. It is suspected that participants may engage in inattentive responding (IR) behavior to combat burden, but the processes underlying this behavior are unclear as previous studies have focused on the barriers to compliance rather than the barriers to providing high-quality data. This study aims to broaden researchers' knowledge about IR during ILD studies using qualitative analysis and uncover the underlying IR processes to aid future hypothesis generation. We explored the process of IR by conducting semistructured qualitative exit interviews with 31 young adult participants (aged 18-29 years) who completed a 12-month ILD health behavior study with daily evening smartphone-based ecological momentary assessment (EMA) surveys and 4-day waves of hourly EMA surveys. The interviews assessed participants' motivations, the impact of time-varying contexts, changes in motivation and response patterns over time, and perceptions of attention check questions (ACQs) to understand participants' response patterns and potential factors leading to IR. Thematic analysis revealed 5 overarching themes on factors that influence participant engagement: (1) friends and family also had to tolerate the frequent surveys, (2) participants tried to respond to surveys quickly, (3) the repetitive nature of surveys led to neutral responses, (4) ACQs within the surveys helped to combat overly consistent response patterns, and (5) different motivations for answering the surveys may have led to different levels of data quality. This study aimed to examine participants' perceptions of the quality of data provided in an ILD study to contribute to the field's understanding of engagement. These findings provide insights into the complex process of IR and participant engagement in ILD studies with EMA. The study identified 5 factors influencing IR that could guide future research to improve EMA survey design. The identified themes offer practical implications for researchers and study designers, including the importance of considering social context, the consideration of dynamic motivations, and the potential benefit of including ACQs as a technique to reduce IR and leveraging the intrinsic motivators of participants. By incorporating these insights, researchers might maximize the scientific value of their multimonth ILD studies through better data collection protocols. RR2-10.2196/36666.

Assessing and accounting for measurement in intensive longitudinal studies: current practices, considerations, and avenues for improvement.

Intensive longitudinal studies, in which participants complete questionnaires multiple times a day over an extended period, are increasingly popular in the social sciences in general and quality-of-life research in particular. The intensive longitudinal methods allow for studying the dynamics of constructs (e.g., how much patient-reported outcomes vary across time). These methods promise higher ecological validity and lower recall bias than traditional methods that question participants only once, since the high frequency means that participants complete questionnaires in their everyday lives and do not have to retrospectively report about a large time interval. However, to ensure the validity of the results obtained from analyzing the intensive longitudinal data (ILD), greater awareness and understanding of appropriate measurement practices are needed. We surveyed 42 researchers experienced with ILD regarding their measurement practices and reasons for suboptimal practices. Results showed that researchers typically do not use measures validated specifically for ILD. Participants assessing the psychometric properties and invariance of measures in their current studies was even less common, as was accounting for these properties when analyzing dynamics. This was mainly because participants did not have the necessary knowledge to conduct these assessments or were unaware of their importance for drawing valid inferences. Open science practices, in contrast, appear reasonably well ingrained in ILD studies. Measurement practices in ILD still need improvement in some key areas; we provide recommendations in order to create a solid foundation for measuring and analyzing psychological constructs.

Evidence for mood instability in patients with bipolar disorder: Applying multilevel hidden Markov modeling to intensive longitudinal ecological momentary assessment data.

Bipolar disorder (BD) is a chronic psychiatric condition characterized by large episodic changes in mood and energy. Recently, BD has been proposed to be conceptualized as chronic cyclical mood instability, as opposed to the traditional view of alternating discrete episodes with stable periods in-between. Recognizing this mood instability may improve care and call for high-frequency measures coupled with advanced statistical models. To uncover empirically derived mood states, a multilevel hidden Markov model (HMM) was applied to 4-month ecological momentary assessment data in 20 patients with BD, yielding ∼9,820 assessments in total. Ecological momentary assessment data comprised self-report questionnaires (5 × daily) measuring manic and depressive constructs. Manic and depressive symptoms were also assessed weekly using the Altman Self-Rating Mania Scale and the Quick Inventory for Depressive Symptomatology Self-Report. Alignment between HMM-uncovered momentary mood states and weekly questionnaires was assessed with a multilevel linear model. HMM uncovered four mood states: neutral, elevated, mixed, and lowered, which aligned with weekly symptom scores. On average, patients remained < 25 hr in one state. In almost half of the patients, mood instability was observed. Switching between mood states, three patterns were identified: patients switching predominantly between (a) neutral and lowered states, (b) neutral and elevated states, and (c) mixed, elevated, and lowered states. In all, elevated and lowered mood states were interspersed by mixed states. The results indicate that chronic mood instability is a key feature of BD, even in "relatively" euthymic periods. This should be considered in theoretical and clinical conceptualizations of the disorder. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Mindfulness and cognitive-behavioral strategies for psychological detachment: Comparing effectiveness and mechanisms of change.

Recovering from work is essential for maintaining occupational well-being, health, motivation, and performance, but recovery is often difficult to achieve. In this study, we evaluated and compared the effectiveness of two (parallel) interventions aimed at promoting recovery: one based on mindfulness and one involving applying cognitive-behavioral strategies. Both interventions were embedded in a measurement burst design, which allowed us to examine the mechanisms underlying change or intervention success. To explore mechanisms of change, we used the stressor-detachment model as a theoretical framework. We operationalized the interventions' effects in three ways: as changes from pretest to posttest, as changes in daily states, and as changes in daily associations. To this end, we used intensive longitudinal data to examine the roles that daily negative activation plays in detachment and strain. In a randomized controlled trial (N = 393), we administered three assessments of traits: pretest, posttest (8 weeks later), and follow-up (3 months after the posttest). We also administered 2 work weeks of experience sampling questionnaires (preintervention and postintervention). Latent change models and Bayes factor equivalence tests revealed that both interventions substantially-and to a similar extent-increased detachment. Bayesian multilevel path models showed improvements in all state variables, including improvements in negative activation, and provided some evidence that mindfulness-based and cognitive-behavioral approaches might tackle different processes at the daily level. We discuss theoretical implications for the literature on recovery from work and specifically for the stressor-detachment model. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The day-level effects of recovery community center attendance on indicators of recovery wellbeing and risk

BackgroundRecovery community centers (RCCs) are a relatively new resource in the recovery support landscape aimed at building their members' recovery capital. In recent years, interest in the value of RCCs has grown, however, no studies have used within-person methods to consider how RCCs may impact the day-to-day lives of their attendees. Using within-person data drawn from members of RCCs, this study examined how visiting RCCs was associated with several same-day indicators of recovery wellbeing and risk: daily sense of meaningfulness, recovery identity, negative affect, and positive affect. MethodsParticipants were 94 visitors of six RCCs in western Pennsylvania. Daily diary methods collected 10 nightly reports of daily RCC attendance and end-of-day meaningfulness, recovery identity, negative affect, and positive affect. Multilevel modeling accounted for nesting in the intensive longitudinal data. In independent models, the study regressed meaningfulness, recovery identity, negative affect, and positive affect onto day- and person-level RCC attendance. ResultsWithin-person associations between RCC attendance and meaningfulness (b = 6.96, SE = 1.66, p < .001), recovery identity (b = 4.75, SE = 1.08, p < .001), and PA (b = 3.82, SE = 1.45, p < .01) were significant, although NA was not (b = −2.41, SE = 1.34, n.s.). All day- by person-level RCC attendance interactions (in preliminary models) and between-person associations were non-significant across recovery outcomes. ConclusionsThe results indicated that on days participants visited RCCs, they reported significantly higher levels of meaningfulness, recovery identity, and positive affect, although negative affect levels did not significantly differ. Also, those who attended RCCs more frequently did not generally report different levels of recovery wellbeing and risk. Taken together, results suggest visiting RCCs works on a daily basis to support interpersonal processes related to positive recovery outcomes. That RCC visits do not appear to reduce negative affect suggests that additional programs may be needed to address negative affect. The within-person design provided insight into the dynamic processes that contribute to the intrapersonal states that support recovery and a practical approach to examining whether and how RCCs might support recovery. By using individuals as their own controls, the study design provided strong counterfactual inference.

Inference with cross-lagged effects-Problems in time.

The interpretation of cross-effects from vector autoregressive models to infer structure and causality among constructs is widespread and sometimes problematic. I describe problems in the interpretation of cross-effects when processes that are thought to fluctuate continuously in time are, as is typically done, modeled as changing only in discrete steps (as in e.g., structural equation modeling)-zeroes in a discrete-time temporal matrix do not necessarily correspond to zero effects in the underlying continuous processes, and vice versa. This has implications for the common case when the presence or absence of cross-effects is used for inference about underlying causal processes. I demonstrate these problems via simulation, and also show that when an underlying set of processes are continuous in time, even relatively few direct causal links can result in much denser temporal effect matrices in discrete-time. I demonstrate one solution to these issues, namely parameterizing the system as a stochastic differential equation and focusing inference on the continuous-time temporal effects. I follow this with some discussion of issues regarding the switch to continuous-time, specifically regularization, appropriate measurement time lag, and model order. An empirical example using intensive longitudinal data highlights some of the complexities of applying such approaches to real data, particularly with respect to model specification, examining misspecification, and parameter interpretation. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Comparing Functional Trend and Learning among Groups in Intensive Binary Longitudinal Eye-Tracking Data using By-Variable Smooth Functions of GAMM.

This paper presents a model specification for group comparisons regarding a functional trend over time within a trial and learning across a series of trials in intensive binary longitudinal eye-tracking data. The functional trend and learning effects are modeled using by-variable smooth functions. This model specification is formulated as a generalized additive mixed model, which allowed for the use of the freely available mgcv package (Wood in Package 'mgcv.' https://cran.r-project.org/web/packages/mgcv/mgcv.pdf , 2023) in R. The model specification was applied to intensive binary longitudinal eye-tracking data, where the questions of interest concern differences between individuals with and without brain injury in their real-time language comprehension and how this affects their learning over time. The results of the simulation study show that the model parameters are recovered well and the by-variable smooth functions are adequately predicted in the same condition as those found in the application.

Examining the proximal relationship between religious coping and depression among trauma-exposed adults

ABSTRACT Depression is a common consequence of sexual victimisation. Although religious coping has been previously studied within the context of sexual victimisation, there is a dearth of research examining the potential reciprocal relationships between religious coping and depression among adults with a history of sexual victimisation at the daily level. The current study addresses this important gap by leveraging an intensive longitudinal design and data analysis to explore bidirectional daily relations between religious coping and depression symptoms in a community sample of adults with a history of sexual victimisation. Participants were 84 individuals with a history of sexual assault who self-identified as religious (M age = 37.43, 67.5% women, 83.1% White). Cross-lagged models showed that prior-day religious coping positively predicted next-day depression symptoms, whereas prior-day depression did not predict next-day religious coping. Findings underscore the potential utility of religious coping in the detection and treatment of depression in this population.