Newborn sleep plays a vital role in early neurological and physiological development and may have lasting consequences for infants' developmental trajectories. However, few methods capture high-density sleep data in naturalistic settings during the newborn period. We address this by assessing the utility of the Owlet Dream Sock for capturing sleep patterns, timing, and duration in the home. Thirty-nine newborns (51.30% female, M age = 9.38 days old, SD = 6.40) wore the Owlet Dream Sock for fourteen days during all sleep periods. Newborns exhibited two common types of sleep bouts, light-only bouts (awake → light sleep → awake sequences) and full sleep cycles (awake → light sleep → deep sleep → light sleep → awake sequences). On average, sleep bouts began around 11:48 AM, and newborns spent approximately 172 monitoring minutes awake, 536 in light sleep, and 54 in deep sleep per day. The duration of time spent awake and in light sleep did not change over the assessment period; however, both the duration of deep sleep and the number of full sleep cycles increased each week. While nighttime sleep minutes did not change, daytime sleep minutes decreased. The Owlet Dream Sock shows acceptable feasibility for capturing high-density newborn sleep data to capture developmental changes in sleep patterns over the first weeks of life.

According to attachment theory, attachment bonds are foundational for subjective well-being. But does that mean that having more attachment relationships is better than having fewer? The current research assessed this question in a cross-sectional study of 4,625 people. We found that the association between the number of targets used for attachment-related needs and well-being was essentially zero. However, exploratory analyses revealed that, among people who had anxious attachments with their attachment figures, having more figures for attachment-related needs was associated with lower subjective well-being. The reverse was found among more secure people (i.e., less anxious) in their attachments. These findings imply that there is not a straightforward relationship between the number of attachment figures people have and their well-being. It is possible that using multiple people for attachment functions may reflect a compensatory process for those who are anxious about their attachments.

Use of recommended sun protection strategies among minor children of melanoma survivors is suboptimal and as a result, these children receive excess ultraviolet radiation exposure that further increases their melanoma risk. Among a sample of 368 melanoma survivor-child dyads, the relationship between family-focused factors and child sun protection and related outcomes was examined, as well as the relations of family factors with individual sun-protective behaviors. The findings indicated that sun protection, tanning, and sunburn were positively associated between survivors and their children. Certain family-focused factors including parental perceived risk for the child to develop melanoma later in life and problem-solving skills were associated with child melanoma risk and protection outcomes. These factors could be important intervention targets to improve sun protection behavior use and skin cancer risk behavior avoidance among children with a familial risk for melanoma. This trial is registered at ClinicalTrials.gov (NCT04201223).

BACKGROUND:Binge eating is a cardinal symptom of binge-eating disorder (BED) and bulimia nervosa (BN), while recurrent purging is specific to BN (American Psychiatric Association, 2022). These behaviors are associated with health consequences (e.g., gastrointestinal, cardiovascular, and metabolic problems; Sheehan & Herman, 2015). The distress-regulating effects of binge and purge behaviors are important targets for intervention. The aims of this examination were to characterize the regulatory dynamics of heart rate (HR) and physical activity following binge and purge behaviors, test whether these dynamics were different from control periods, and test whether these dynamics were different from one another. METHODS:A subsample of participants (n = 295) from the BEGIN study reported binges and purges using the Recovery Record app for 30 days; Apple Watch devices were used to measure steps and HR. Regulatory dynamics of the velocity and acceleration of HR and steps were modeled using multilevel models. RESULTS:Regulatory dynamics of HR were different following binges (dysregulated) relative to control periods (homeostatic) and relative to those following purges (mixed). Regulatory dynamics of steps were significantly different following binges (dysregulated) and following purges (homeostatic) relative to control periods (different type of homeostatic) as well as with respect to one another. CONCLUSIONS:Regulatory dynamics of HR and physical activity can be captured using smart watches and modeled using systems models. Regulatory dynamics of HR have promise as a marker of treatment progress in reducing binge eating frequency, and regulatory dynamics of steps have promise as a digital marker of binge and purge behaviors. PUBLIC SIGNIFICANCE:Measures from commercial smart watches are able to detect changes in the complex patterning of heart rate and physical activity following binge eating and purging for individuals with BED. Given the cycle of past dynamics relating to future dynamics, the results suggest that these commercial devices could be useful for tracking treatment progress in reducing binge and purge frequency.

Pain is an inherently negative perceptual and affective experience that acts as a warning system to protect the body from injury and illness. Pain unfolds over time and is influenced by myriad factors, making it highly dynamic. Despite this, statistical measures often treat any intraindividual variability in pain ratings as noise or error. This is consequential, especially for research on chronic pain, because pain variability is associated with greater pain severity and depression. Yet, differences in pain variability between patients with chronic pain and controls in response to acute pain has not been fully examined-and it is unknown if dispositional factors such as pain catastrophizing (negative cognitive-affective response to potential or actual pain in which attention cannot be diverted away from pain) relate to pain variability. In the current study, we recruited chronic-pain patients (N = 30) and pain-free controls (N = 22) to complete a 30-second thermal pain task where they continually rated a painful thermal stimulus. To quantify pain variability and capture potential dynamics, we used both a traditional intraindividual standard deviation (iSD) metric of variability and a novel derivatives approach. For both metrics, patients with chronic pain had higher variability in their pain ratings over time, and pain catastrophizing significantly mediated this relationship. This suggests patients with chronic pain experience pain stimuli differently over time, and pain catastrophizing may account for this differential experience. PERSPECTIVE: The present study demonstrates (using multiple variability metrics) that chronic pain patients show more variability when rating experimental pain stimuli, and that pain catastrophizing helps explain this differential experience. These results provide preliminary evidence that short-term pain variability could have utility as a clinical marker in pain assessment and treatment.

Recent studies suggest that the EEG aperiodic exponent (often represented as a slope in log-log space) is sensitive to individual differences in momentary cognitive skills such as selective attention and information processing speed. However, findings are mixed, and most of the studies have focused on just a narrow range of cognitive domains. This study used an archival dataset to help clarify associations between resting aperiodic features and broad domains of cognitive ability, which vary in their demands on momentary processing. Undergraduates (N = 166) of age 18-52 years completed a resting EEG session as well as a standardized, individually administered assessment of cognitive ability that included measures of processing speed, working memory, and higher-order visuospatial and verbal skills. A subsample (n = 110) also completed a computerized reaction time task with three difficulty levels. Data reduction analyses revealed strong correlations between the aperiodic offset and slope across electrodes, and a single component accounted for ~60% of variance in slopes across the scalp, in both eyes-closed and eyes-open conditions. Structural equation models did not support relations between the slope and specific domains tapping momentary processes. However, secondary analyses indicated that the eyes-open slope was related to higher overall performance, as represented by a single general ability factor. A latent reaction time variable was significantly inversely related to both eyes-closed and eyes-open resting exponents, such that faster reaction times were associated with steeper slopes. These findings support and help clarify the relation of the resting EEG exponent to individual differences in cognitive skills.

Objective: Personality changes across the life span. Life events, such as marriage, becoming a parent, and retirement, have been proposed as facilitating personality growth via the adoption of novel social roles. However, empirical evidence linking life events with personality development is sparse. Most studies have relied on few assessments separated by long time intervals and have focused on a single life event. In contrast, the content of life is composed of small, recurrent experiences (e.g., getting sick or practicing a hobby), with relatively few major events (e.g., childbirth). Small, frequently experienced life events may play an important and overlooked role in personality development.Method: The present study examined the extent to which 25 major and minor life events alter the trajectory of personality development in a large, frequently assessed sample (N-sample = 4904, N-assessments = 47,814, median retest interval = 35 days).Results: Using a flexible analytic strategy to accommodate the repeated occurrence of life events, we found that the trajectory of personality development shifted in response to a single occurrence of some major life events (e.g., divorce), and recurrent, "minor" life experiences (e.g., one's partner doing something special).Conclusion: Both stark role changes and frequently reinforced minor experiences can lead to personality change.

Social and behavioral scientists are increasingly interested the dynamics of the processes they study. Despite the wide array of processes studied, a fairly narrow set of models are applied to characterize dynamics within these processes. For social and behavioral research to take the next step in modeling dynamics, a wider variety of models need to be considered. The reservoir model is one model of psychological regulation that helps expand the models available (Deboeck & Bergeman, 2013). The present article implements the Bayesian reservoir model for both single time series and multilevel data. Simulation 1 compares the performance of the original version of the reservoir model fit using structural equation modeling (Deboeck & Bergeman, 2013) to the proposed Bayesian estimation approach. Simulation 2 expands this to a multilevel data scenario and compares this to the single-level version. The Bayesian estimation approach performs substantially better than the original estimation approach and produces low-bias estimates even with time series as short as 25 observations. Combining Bayesian estimation with a multilevel modeling approach allows for relatively unbiased estimation with sample sizes as small as 15 individuals and/or with time series as short as 15 observations. Finally, a substantive example is presented that applies the Bayesian reservoir model to perceived stress, examining how the model parameters relate to psychological variables commonly expected to relate to resilience. The current expansion of the reservoir model demonstrates the benefits of leveraging the combined strengths of Bayesian estimation and multilevel modeling, with new dynamic models that have been tailored to match the process of psychological regulation.

In this study, we investigate using passive data, specifically heart rate and actigraphy, for individuals with binge-type eating disorders such as bulimia nervosa (BN) and binge-eating disorder (BED). By applying dynamical-system theory and incorporating advancements in technology-based health care, we explored the relationship between passive data patterns as potential indicators of binge-eating episodes. Over 30 days, 1,019 participants with BN or BED symptoms used the Recovery Record app on iPhone and Apple Watches for real-time eating-behavior logging. Apple Watches simultaneously recorded heart rate and actigraphy. Results show no marked difference in heart and step averages 2 hr before a binge versus a control period. However, significant momentum and stability differences emerged when examining the changing dynamics leading up to a binge event. These findings suggest that the stability of step, rather than their average value, may serve as a detectable indicator of approaching binge events.

Existing work on the contribution of life events and person characteristics to changes in attachment has mostly overlooked interactions between events and characteristics. Using 15 common events and ten personality characteristics in a multi-wave longitudinal study of 6,566 people, we examined whether person characteristics moderate the impact of life events on change in attachment. Although we found more interactions than were expected by chance, they did not consistently involve specific events or person characteristics and had small effect sizes. The largest number of event-person interactions were observed for changes in attachment security, followed by anxiety and avoidance. We found a similar number of interactions between events and within-person variation in person characteristics and "traditional" PxE interactions where the person characteristics are stable. These results suggest the need to look at both the traditional PxE interactions and the way dynamically varying person characteristics interact with events to understand changes in attachment.

According to the canalization hypothesis of attachment theory (Bowlby, 1973), people's trajectories of attachment security should become increasingly stable and buffered against external pressures as their relationships progress. The present study aimed to examine this hypothesis within the context of romantic relationships. We analyzed longitudinal data collected from 1,741 adults who completed between three and 24 survey assessments (average number of waves analyzed = 6.79, SD = 5.31; median test-retest interval = 35 days). We modeled participants' within-person fluctuations in partner-specific security as a function of their romantic relationship length. Additionally, we examined whether attachment-related events (e.g., conflict with one's partner) predict greater within-person fluctuations in security among people involved in newer versus more established romantic relationships. Our results suggest that people in newer romantic relationships demonstrated greater fluctuations in partner-specific attachment anxiety-both generally and in reaction to attachment-related events-compared to those in well-established romantic relationships. However, neither of these trends was observed for partner-specific attachment avoidance. These results provide partial support for the canalization hypothesis but also suggest that canalization processes may be more nuanced than previously assumed.

Background: Children of parents who had melanoma are more likely to develop skin cancer themselves owing to shared familial risks. The prevention of sunburns and promotion of sun-protective behaviors are essential to control cancer among these children. The Family Lifestyles, Actions and Risk Education (FLARE) intervention will be delivered as part of a randomized controlled trial to support parent-child collaboration to improve sun safety outcomes among children of melanoma survivors. Methods: FLARE is a two-arm randomized controlled trial design that will recruit dyads comprised of a parent who is a melanoma survivor and their child (aged 8-17 years). Dyads will be randomized to receive FLARE or standard skin cancer prevention education, which both entail 3 telehealth sessions with an interventionist. FLARE is guided by Social-Cognitive and Protection Motivation theories to target child sun protection behaviors through parent and child perceived risk for melanoma, problem-solving skills, and development of a family skin protection action plan to promote positive modeling of sun protection behaviors. At multiple assessments through one-year post-baseline, parents and children complete surveys to assess frequency of reported child sunburns, child sun protection behaviors and melanin-induced surface skin color change, and potential mediators of intervention effects (e.g., parent-child modeling). Conclusion: The FLARE trial addresses the need for melanoma preventive interventions for children with familial risk for the disease. If efficacious, FLARE could help to mitigate familial risk for melanoma among these children by teaching practices which, if enacted, decrease sunburn occurrence and improve children's use of wellestablished sun protection strategies.

Developmental researchers commonly utilize longitudinal data to decompose reciprocal and dynamic associations between repeatedly measured constructs to better understand the temporal precedence between constructs. Although the cross-lagged panel model (CLPM) is commonly used in developmental research, it has been criticized for its potential to produce biased estimates due to the fact of ignoring the trait-like, time-invariant nature of stability in constructs across people and aggregating the between- and within-person effects together as a single estimate. Recently, a growing set of alternatives have emerged, but the estimates across CLPM and alternatives have rarely been compared in developmental research. The primary purpose of this article is to (a) provide a three-component framework to help developmental researchers to select and specify the most appropriate model, and (b) illustrate how models differ in estimates with an empirical example investigating the reciprocal associations between internalizing and externalizing problems in school-aged children. Methods: We specified CLPM, Random-Intercept Cross-lagged Panel Model (RI-CLPM), Latent Curve Model with Structured Residuals (LCM-SR), Latent Change Score (LCS), and a Random-Mean LCS using four waves of data from ECLS-K: 2011 (N = 8779). Results: The CLPM provided the most evidence of significant cross-lagged paths but the poorest fit to the data compared to other models. Alternative models had excellent fit and found either only negative temporal precedence from internalizing to externalizing problems or simply no evidence of prospective within-person relations between internalizing and externalizing problems.

Children develop and learn within dynamic contexts, yet the simplifying assumptions of common statistical methods often relegate such complexity to unexplained error. This chapter discusses ideas from the dynamic systems literature, which focuses on the interplay within and between components of complex systems, such as individuals and their multitiered contexts. This chapter presents three scenarios highlighting knowledge from the dynamic systems literature. These scenarios have implications requiring reconsidering common approaches to explaining the variance associated with change, the conceptualization of effect sizes, and the use of between-person data and analyses to make within-person inferences. The final section provides resources for moving beyond dynamic metaphors and principles, so theories can be translated into testable hypotheses.

Background Data 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. Objective We 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. Methods The 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. Results Data 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. Conclusions We 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 Registration ClinicalTrials.gov NCT04162574; https://clinicaltrials.gov/ct2/show/NCT04162574 International Registered Report Identifier (IRRID) DERR1-10.2196/38294

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's < .05; pseudo-R2 McFadden 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's < .05; pseudo-R2 McFadden  = .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.

Scholars have documented the research tradeoffs of using ecological momentary assessments (EMA) to study daily life. Less is known about participants’ perspectives. The aim of this research note was to delineate the challenges of participating in a 14-day EMA study of family leisure described by 41 individuals (mothers, fathers, and adolescents) from 14 families to inform future family leisure studies using EMA. Participants identified three potential challenges: repetitive questions, inconvenient signals, and short response windows. Despite these challenges, the majority of participants indicated that they would recommend a similar study to others. The reality that so many families feel squeezed for time requires scholars to be aware of participants’ perspectives and make accommodations accordingly. Otherwise, advances in family leisure research and programs will be thwarted. Ecologically valid, yet time-intensive, research methods like EMA could provide rich data about how to promote fulfilling family leisure experiences and well-being.

Attachment theorists suggest that people construct a number of distinct working models throughout life. People develop global working models, which reflect their expectations and beliefs concerning relationships in general, as well as relationship-specific working models of close others—their mothers, fathers, romantic partners, and friends. The present research investigated the interplay of these different working models over time. We analyzed longitudinal data collected from 4,904 adults (mean age = 35.24 years; SD = 11.63) who completed between 3 and 24 online survey assessments (median test–retest interval = 35 days). Using latent growth curve modeling, we examined the associations among both long-term changes and short-term fluctuations in participants’ working models. Our findings suggest that different working models not only change together over the long run, but also exhibit co-occurring, short-term fluctuations. This was true concerning the associations between global and relationship-specific models as well as among different relationship-specific models.

During pregnancy, a woman's emotions can have longstanding implications for both her own and her child's health. Within-person emotional concordance refers to the simultaneous measurement of emotional responses across multiple levels of analysis. This method may provide insight into how pregnant women experience emotions in response to stress. We enrolled 162 pregnant women and assessed concordance through autonomic physiology (electrodermal activity [EDA], respiratory sinus arrhythmia [RSA]), and coded behavior (Pmsocial, Flight, Displacement) during the Trier Social Stress Test-Speech. We used multilevel models to examine behavioral-physiological concordance and whether self-reported emotion dysregulation moderated these effects. Participants exhibited EDA-Prosocial concordance, suggesting that prosocial behavior may be a marker of stress. Emotion dysregulation did not moderate concordance. These findings provide novel information about behavioral coping to stress in pregnancy. Given the importance of observed behavior in the maintenance and treatment of psychopathology, these findings may provide a launchpad for future perinatal intervention research.