Experiments in the wild. Introducing the within-person encouragement design

The within-person encouragement design introduced here combines methodological approaches from three research traditions: (a) the analysis of within-person couplings using multilevel models, (b) the experimental manipulation of a treatment variable at the within-person level, and (c) the use of random encouragements as instrumental variables to induce exogenous experimental variation when strict treatment adherence is unrealistic. The proposed combination of these approaches opens up new possibilities to study treatment effects of a broad range of behavioral variables in realistic everyday contexts. We introduce this new research design together with a corresponding data analysis framework: instrumental variable estimation with two-level structural equation models. Using simulations, we show that the approach is applicable with feasible design dimensions regarding numbers of measurement occasions and participants and realistic assumptions about adherence to the encouragement conditions. Possible applications and extensions, as well as potential problems and limitations are discussed. (DIPF/Orig.

Studying within-person variation and within-person couplings in intensive longitudinal data. Lessons learned and to be learned

Intensive longitudinal designs (e.g., experience sampling methods, daily diary studies, or ambulatory assessments) continue to gain importance in sychological aging research. Empirical research using these designs has greatly facilitated our understanding of short-term within-person processes and has started to approach the question how these processes shape long-term development across the life span. The aim of this viewpoint article is to point out four key issues in intensive longitudinal designs that in our opinion require more attention than they are currently given: (a) improvement in measurement reliability, (b) the necessity to investigate inter-individual differences in short-term dynamics, (c) considerations of the time scale across which dynamic effects unfold, and (d) targeting causality by incorporating experimental methods in intensive longitudinal designs. We illustrate these four key issues by referring to a prominent example of within-person dynamics in prior empirical research: the within-person coupling of stressor occurrence and well-being stress reactivity). (DIPF/Orig.

Tracking Infant Development With a Smartphone:A Practical Guide to the Experience Sampling Method

The COVID-19 pandemic has forced developmental researchers to rethink their traditional research practices. The growing need to study infant development at a distance has shifted our research paradigm to online and digital monitoring of infants and families, using electronic devices, such as smartphones. In this practical guide, we introduce the Experience Sampling Method (ESM) – a research method to collect data, in the moment, on multiple occasions over time – for examining infant development at a distance. ESM is highly suited for assessing dynamic processes of infant development and family dynamics, such as parent-infant interactions and parenting practices. It can also be used to track highly fluctuating family dynamics (e.g., infant and parental mood or behavior) and routines (e.g., activity levels and feeding practices). The aim of the current paper was to provide an overview by explaining what ESM is and for what types of research ESM is best suited. Next, we provide a brief step-by-step guide on how to start and run an ESM study, including preregistration, development of a questionnaire, using wearables and other hardware, planning and design considerations, and examples of possible analysis techniques. Finally, we discuss common pitfalls of ESM research and how to avoid them

Single-Subject Research in Psychiatry:Facts and Fictions

Scientific evidence in the field of psychiatry is mainly derived from group-based ("nomothetic") studies that yield group-aggregated results, while often the need is to answer questions that apply to individuals. Particularly in the presence of great inter-individual differences and temporal complexities, information at the individual-person level may be valuable for personalized treatment decisions, individual predictions and diagnostics. The single-subject study design can be used to make inferences about individual persons. Yet, the single-subject study is not often used in the field of psychiatry. We believe that this is because of a lack of awareness of its value rather than a lack of usefulness or feasibility. In the present paper, we aimed to resolve some common misconceptions and beliefs about single-subject studies by discussing some commonly heard "facts and fictions." We also discuss some situations in which the single-subject study is more or less appropriate, and the potential of combining single-subject and group-based study designs into one study. While not intending to plea for single-subject studies at the expense of group-based studies, we hope to increase awareness of the value of single-subject research by informing the reader about several aspects of this design, resolving misunderstanding, and providing references for further reading

Unraveling the interplay between daily life fatigue and physical activity after subarachnoid hemorrhage: an ecological momentary assessment and accelerometry study

Background Fatigue is one of the most commonly reported symptoms after subarachnoid hemorrhage (SAH) and is indirectly associated with physical activity (PA). Associations between fatigue and PA are primarily examined based on conventional measures (i.e. a single fatigue score or average PA levels), thereby assuming that fatigue and PA do not fluctuate over time. However, levels of fatigue and PA may not be stable and may interrelate dynamically in daily life. Insight in direct relationships between fatigue and PA in daily life, could add to the development of personalized rehabilitation strategies. Therefore we aimed to examine bidirectional relationships between momentary fatigue and PA in people with SAH. Methods People (n = 38) with SAH who suffer from chronic fatigue were included in an observational study using Ecological Momentary Assessment (EMA) and accelerometry. Momentary fatigue was assessed on a scale from 1 to 7 (no to extreme fatigue), assessed with 10–11 prompts per day for 7 consecutive days using EMA with a mobile phone. PA was continuously measured during this 7-day period with a thigh-worn Activ8 accelerometer and expressed as total minutes of standing, walking, running and cycling in a period of 45 min before and after a momentary fatigue prompt. Multilevel mixed model analyses including random effects were conducted. Results Mean age was 53.2 years (SD = 13.4), 58% female, and mean time post SAH onset was 9.5 months (SD = 2.1). Multilevel analyses with only time effects to predict fatigue and PA revealed that fatigue significantly (p < 0.001) increased over the day and PA significantly (p < 0.001) decreased. In addition, more PA was significantly associated with higher subsequent fatigue (β = 0.004, p < 0.05) and higher fatigue was significantly associated with less subsequent PA (β=-0.736, p < 0.05). Moreover, these associations significantly differed between participants (p < 0.001). Conclusions By combining EMA measures of fatigue with accelerometer-based PA we found that fatigue and PA are bidirectionally associated. In addition, these associations differ among participants. Given these different bidirectional associations, rehabilitation aimed at reducing fatigue should comprise personalized strategies to improve both fatigue and PA simultaneously, for example by combining exercise therapy with cognitive behavioral and/or energy management therapy

Multilevel Modeling of Interval-Contingent Data In Neuropsychology Research Using the \u3ci\u3eImerTest\u3c/i\u3e Package In R

Intensive longitudinal research designs are becoming more common in the field of neuropsychology. They are a powerful approach to studying development and change in naturally occurring phenomena. However, to fully capitalize on the wealth of data yielded by these designs, researchers have to understand the nature of multilevel data structures. The purpose of the present article is to describe some of the basic concepts and techniques involved in modeling multilevel data structures. In addition, this article serves as a step-by-step tutorial to demonstrate how neuropsychologists can implement basic multilevel modeling techniques with real data and the R package, lmerTest. R may be an ideal option for some empirical scientists, applied statisticians, and clinicians, because it is a free and open-source programming language for statistical computing and graphics that offers a flexible and powerful set of tools for analyzing data. All data and code described in the present article have been made publicly available

Drivers of productivity: Being physically active increases yet sedentary bouts and lack of sleep decrease work ability

Physical behavior (ie, physical activity, sedentary behavior, and sleep) is a crucial lifestyle factor for preventing and managing diseases across the lifespan. However, less is known about potential work-related psychological and cognitive outcomes such as productivity. The present study examined within-person associations between physical behavior and self-perceived work ability. To investigate the degree to which physical behavior parameters influence self-perceived work ability in everyday life, we conducted an Ambulatory Assessment study in 103 university students over 5 days. Physical behavior was assessed continuously via a multi-sensor system. Self-perceived work ability was assessed repeatedly up to six times per day on smartphones. We employed multilevel modeling to analyze the within-person effects of physical behavior on self-perceived work ability. Physical activity intensity (MET) (β = 0.15 ± 0.06, t = 2.59, p = 0.012) and sit-to-stand transitions (β = 0.07 ± 0.03, t = 2.44, p = 0.015) were positively associated with self-perceived work ability. Sedentary bouts (≥20 min) (β = −0.21 ± 0.08, t = −2.74, p = 0.006) and deviation from a recommended sleep duration (ie, 8 h) (β = −0.1 ± 0.04, t = −2.38, p = 0.018) were negatively associated with self-perceived work ability. Exploratory analyses supported the robustness of our findings by comparing various time frames. Total sedentary time and sleep quality were not associated with self-perceived work ability. Regular sleep durations, breaking up sedentary time through sit-to-stand transitions, and higher intensities of physical activity may be important for the regulation of self-perceived work ability in university students’ daily lives

The Effect of Collective Efficacy and Neighborhood Structural Disadvantage on Depressive Symptoms among Adolescents in the United States

The purpose of this dissertation was to examine (1) the moderating role of parental neighborhood perceptions on the relationship between neighborhood structural disadvantage and adolescent depressive symptoms, (2) if adolescent neighborhood perceptions moderated the association between neighborhood structural disadvantage and adolescent depressive symptoms, and (3) the effects of neighborhood structural disadvantage on depressive symptom trajectories as well as the moderating role of neighborhood perceptions on the relationship from adolescence to young adulthood. Data came from the National Longitudinal Study of Adolescent to Adult Health (Add Health) (N=12,105), and random effects multilevel modeling along with growth curve modeling were used. Results showed that parental-perceived neighborhood disorder was significantly associated with higher levels of adolescent depressive symptoms (β=0.27, SE=0.05, p≤0.001), while adolescent-perceived neighborhood social cohesion (β=0.24, SE=0.04, p≤0.001) and safety (β=0.47, SE=0.04, p≤0.001) were significantly associated with lower depressive symptoms among adolescents after full adjustment. Parental-perceived collective efficacy was not associated with adolescent depressive symptoms (p\u3e0.05). Interactions between neighborhood concentrated poverty and parental-perceived neighborhood disorder, adolescent-perceived collective efficacy, contentment, and safety were also significant (p≤0.05). Parental-perceived collective efficacy was not found to be a moderator (p\u3e0.05). Findings suggest that aspects of the neighborhood social environment may help to buffer against depression, particularly in high poverty neighborhoods. Components of neighborhood structural disadvantage and disorder, collective efficacy, contentment, and safety could serve as targets for the development of structural and other intervention strategies such as community-level interventions, aimed at reducing or preventing depression. Ultimately, addressing neighborhood structural disadvantage and improving the social environment may help to reduce depressive symptoms among adolescents as well as depression prevalence and risk, thereby reducing the growing mental health burden among youth

Three Essays on Correlated Binary Outcomes: Detection and Appropriate Models

abstract: Correlation is common in many types of data, including those collected through longitudinal studies or in a hierarchical structure. In the case of clustering, or repeated measurements, there is inherent correlation between observations within the same group, or between observations obtained on the same subject. Longitudinal studies also introduce association between the covariates and the outcomes across time. When multiple outcomes are of interest, association may exist between the various models. These correlations can lead to issues in model fitting and inference if not properly accounted for. This dissertation presents three papers discussing appropriate methods to properly consider different types of association. The first paper introduces an ANOVA based measure of intraclass correlation for three level hierarchical data with binary outcomes, and corresponding properties. This measure is useful for evaluating when the correlation due to clustering warrants a more complex model. This measure is used to investigate AIDS knowledge in a clustered study conducted in Bangladesh. The second paper develops the Partitioned generalized method of moments (Partitioned GMM) model for longitudinal studies. This model utilizes valid moment conditions to separately estimate the varying effects of each time-dependent covariate on the outcome over time using multiple coefficients. The model is fit to data from the National Longitudinal Study of Adolescent to Adult Health (Add Health) to investigate risk factors of childhood obesity. In the third paper, the Partitioned GMM model is extended to jointly estimate regression models for multiple outcomes of interest. Thus, this approach takes into account both the correlation between the multivariate outcomes, as well as the correlation due to time-dependency in longitudinal studies. The model utilizes an expanded weight matrix and objective function composed of valid moment conditions to simultaneously estimate optimal regression coefficients. This approach is applied to Add Health data to simultaneously study drivers of outcomes including smoking, social alcohol usage, and obesity in children.Dissertation/ThesisDoctoral Dissertation Statistics 201

Digital health in ambulatory assessment

In this volume all accepted contributions to the 5th Biennial Conference of the Society for Ambulatory Assessment are published. The number and quality of these contributions testify to the high standard of international research in ambulatory monitoring, the rapid advances in technology and data handling supporting ambulatory assessment, and the importance of these developments for the rapidly expanding area of Digital Health. Converging technologies such as Internet applications, social networks, smartphones and wearable sensors in the area of health, are now beginning to transform our approach to health research, healthcare, and communication and access to information