Attrition in developmental psychology: A review of modern missing data reporting and practices

Inherent in applied developmental sciences is the threat to validity and generalizability due to missing data as a result of participant drop-out. The current paper provides an overview of how attrition should be reported, which tests can examine the potential of bias due to attrition (e.g., t-tests, logistic regression, Little\u27s MCAR test, sensitivity analysis), and how it is best corrected through modern missing data analyses. To amend this discussion of best practices in managing and reporting attrition, an assessment of how developmental sciences currently handle attrition was conducted. Longitudinal studies (n = 541) published from 2009-2012 in major developmental journals were reviewed for attrition reporting practices and how authors handled missing data based on recommendations in the Publication Manual of the American Psychological Association (APA, 2010). Results suggest attrition reporting is not following APA recommendations, quality of reporting did not improve since the APA publication, and a low proportion of authors provided sufficient information to convey that data properly met the MAR assumption. An example based on simulated data demonstrates bias that may result from various missing data mechanisms in longitudinal data, the utility of auxiliary variables for the MAR assumption, and the need for viewing missingness along a continuum from MAR to MNAR

Integrating Developmental Theory and Methodology: Using Derivatives to Articulate Change Theories, Models, and Inferences

Matching theories about growth, development, and change to appropriate statistical models can present a challenge, which can result in misuse, misinterpretation, and underutilization of different analytical approaches. We discuss the use of derivatives: the change of a construct with respect to the change in another construct. Derivatives provide a common language linking developmental theory and statistical methods. Conceptualizing change in terms of derivatives allows precise translation of theory into method and highlights commonly overlooked models of change. A wide variety of models can be understood in terms of the level, velocity, and acceleration of constructs: the zeroth, first, and second derivatives, respectively. We introduce the language of derivatives, and highlight the conceptually differing questions that can be addressed in developmental studies. A substantive example is presented to demonstrate how common and unfamiliar statistical methodology can be understood as addressing relations between differing pairs of derivatives

Synchronization in Dancing is Not Winner--Takes--All: Ambiguity Persists in

al Symmetry Between Dancers Steven M. Boker # Eric Covey Stacey Tiberio Pascal Deboeck May 4, 2005 Symmetry formation, symmetry breaking, and the strength of symmetric coupling in social interaction are investigated using motion capture data from pairs of individuals dancing to repeating rhythms. Repeating auditory rhythms are a simple form of temporal symmetry in which local entropy can be controlled. Spatio--temporal symmetry is formed when an individual performs cyclic movements, such as dancing to a repeating rhythm. Social spatio--temporal symmetry is formed when two individuals dance together. Rhythmic patterns can be ambiguous, having two or more segmentation points that listeners might perceive as the beginning of a rhythmic pattern. But subjects report hearing only one organization, implying that temporal symmetry is Gestalt--like: a winner--takes--all process. In the present study, the degree of temporal ambiguity in auditory stimuli was found to have a significant e#ect

Passive Sensor Data for Characterizing States of Increased Risk for Eating Disorder Behaviors in the Digital Phenotyping Arm of the Binge Eating Genetics Initiative: Protocol for an Observational Study

BackgroundData that can be easily, efficiently, and safely collected via cell phones and other digital devices have great potential for clinical application. Here, we focus on how these data could be used to refine and augment intervention strategies for binge eating disorder (BED) and bulimia nervosa (BN), conditions that lack highly efficacious, enduring, and accessible treatments. These data are easy to collect digitally but are highly complex and present unique methodological challenges that invite innovative solutions. ObjectiveWe describe the digital phenotyping component of the Binge Eating Genetics Initiative, which uses personal digital device data to capture dynamic patterns of risk for binge and purge episodes. Characteristic data signatures will ultimately be used to develop personalized models of eating disorder pathologies and just-in-time interventions to reduce risk for related behaviors. Here, we focus on the methods used to prepare the data for analysis and discuss how these approaches can be generalized beyond the current application. MethodsThe University of North Carolina Biomedical Institutional Review Board approved all study procedures. Participants who met diagnostic criteria for BED or BN provided real time assessments of eating behaviors and feelings through the Recovery Record app delivered on iPhones and the Apple Watches. Continuous passive measures of physiological activation (heart rate) and physical activity (step count) were collected from Apple Watches over 30 days. Data were cleaned to account for user and device recording errors, including duplicate entries and unreliable heart rate and step values. Across participants, the proportion of data points removed during cleaning ranged from <0.1% to 2.4%, depending on the data source. To prepare the data for multivariate time series analysis, we used a novel data handling approach to address variable measurement frequency across data sources and devices. This involved mapping heart rate, step count, feeling ratings, and eating disorder behaviors onto simultaneous minute-level time series that will enable the characterization of individual- and group-level regulatory dynamics preceding and following binge and purge episodes. ResultsData collection and cleaning are complete. Between August 2017 and May 2021, 1019 participants provided an average of 25 days of data yielding 3,419,937 heart rate values, 1,635,993 step counts, 8274 binge or purge events, and 85,200 feeling observations. Analysis will begin in spring 2022. ConclusionsWe provide a detailed description of the methods used to collect, clean, and prepare personal digital device data from one component of a large, longitudinal eating disorder study. The results will identify digital signatures of increased risk for binge and purge events, which may ultimately be used to create digital interventions for BED and BN. Our goal is to contribute to increased transparency in the handling and analysis of personal digital device data. Trial RegistrationClinicalTrials.gov NCT04162574; https://clinicaltrials.gov/ct2/show/NCT04162574 International Registered Report Identifier (IRRID)DERR1-10.2196/3829

Retention, Engagement, and Binge-Eating Outcomes: Evaluating Feasibility of the Binge-Eating Genetics Initiative Study

OBJECTIVE: Using preliminary data from the Binge-Eating Genetics Initiative (BEGIN), we evaluated the feasibility of delivering an eating disorder digital app, Recovery Record, through smartphone and wearable technology for individuals with binge-type eating disorders. METHODS: Participants (n = 170; 96% female) between 18 and 45 years old with lived experience of binge-eating disorder or bulimia nervosa and current binge-eating episodes were recruited through the Recovery Record app. They were randomized into a Watch (first-generation Apple Watch + iPhone) or iPhone group; they engaged with the app over 30 days and completed baseline and endpoint surveys. Retention, engagement, and associations between severity of illness and engagement were evaluated. RESULTS: Significantly more participants in the Watch group completed the study (p = .045); this group had greater engagement than the iPhone group (p\u27s \u3c .05; pseudo-R effect size = .01-.34). Overall, binge-eating episodes, reported for the previous 28 days, were significantly reduced from baseline (mean = 12.3) to endpoint (mean = 6.4): most participants in the Watch (60%) and iPhone (66%) groups reported reduced binge-eating episodes from baseline to endpoint. There were no significant group differences across measures of binge eating. In the Watch group, participants with fewer episodes of binge eating at baseline were more engaged (p\u27s \u3c .05; pseudo-R = .01-.02). Engagement did not significantly predict binge eating at endpoint nor change in binge-eating episodes from baseline to endpoint for both the Watch and iPhone groups. DISCUSSION: Using wearable technology alongside iPhones to deliver an eating disorder app may improve study completion and app engagement compared with using iPhones alone

Modeling Noisy Data with Differential Equations Using Observed and Expected Matrices

intraindividual variability, differential equation model(s)(ing), time series, damped linear oscillator, analytic solution(s),