Synchrony between interacting systems is an important area of nonlinear dynamics in physical systems. Recently psychological researchers from multiple areas of psychology have become interested in nonverbal synchrony (i.e., coordinated motion between two individuals engaged in dyadic information exchange such as communication or dance) as a predictor and outcome of psychological processes. An important step in studying nonverbal synchrony is systematically and validly differentiating synchronous systems from nonsynchronous systems. However, many current methods of testing and quantifying nonverbal synchrony will show some level of observed synchrony even when research participants have not interacted with one another. In this article we demonstrate the use of surrogate data generation methodology as a means of testing new null-hypotheses for synchrony between bivariate time series such as those derived from modern motion tracking methods. Hypotheses generated by surrogate data generation methods are more nuanced and meaningful than hypotheses from standard null-hypothesis testing. We review four surrogate data generation methods for testing for significant nonverbal synchrony within a windowed cross-correlation (WCC) framework. We also interpret the null-hypotheses generated by these surrogate data generation methods with respect to nonverbal synchrony as a specific use of surrogate data generation, which can then be generalized for hypothesis testing of other psychological time series. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

One of the promises of the experience sampling methodology (ESM) is that a statistical analysis of an individual's emotions, cognitions and behaviors in everyday-life could be used to identify relevant treatment targets. A requisite for clinical implementation is that outcomes of such person-specific time-series analyses are not wholly contingent on the researcher performing them. Methods To evaluate this, we crowdsourced the analysis of one individual patient's ESM data to 12 prominent research teams, asking them what symptom(s) they would advise the treating clinician to target in subsequent treatment. Results Variation was evident at different stages of the analysis, from preprocessing steps (e.g., variable selection, clustering, handling of missing data) to the type of statistics and rationale for selecting targets. Most teams did include a type of vector autoregressive model, examining relations between symptoms over time. Although most teams were confident their selected targets would provide useful information to the clinician, not one recommendation was similar: both the number (0-16) and nature of selected targets varied widely. Conclusion This study makes transparent that the selection of treatment targets based on personalized models using ESM data is currently highly conditional on subjective analytical choices and highlights key conceptual and methodological issues that need to be addressed in moving towards clinical implementation.

The accurate identification of the content and number of latent factors underlying multivariate data is an important endeavor in many areas of Psychology and related fields. Recently, a new dimensionality assessment technique based on network psychometrics was proposed (Exploratory Graph Analysis, EGA), but a measure to check the fit of the dimensionality structure to the data estimated via EGA is still lacking. Although traditional factor-analytic fit measures are widespread, recent research has identified limitations for their effectiveness in categorical variables. Here, we propose three new fit measures (termed entropy fit indices) that combines information theory, quantum information theory and structural analysis: Entropy Fit Index (EFI), EFI with Von Neumman Entropy (EFI.vn) and Total EFI.vn (TEFI.vn). The first can be estimated in complete datasets using Shannon entropy, while EFI.vn and TEFI.vn can be estimated in correlation matrices using quantum information metrics. We show, through several simulations, that TEFI.vn, EFI.vn and EFI are as accurate or more accurate than traditional fit measures when identifying the number of simulated latent factors. However, in conditions where more factors are extracted than the number of factors simulated, only TEFI.vn presents a very high accuracy. In addition, we provide an applied example that demonstrates how the new fit measures can be used with a real-world dataset, using exploratory graph analysis.

The contribution of nature versus nurture to the development of human behavior has been debated for centuries. Here, we offer a piece to this complex puzzle by identifying the human endogenous oxytocin system—known for its critical role in mammalian sociality—as a system sensitive to its early environment and subject to epigenetic change. Recent animal work suggests that early parental care is associated with changes in DNA methylation of conserved regulatory sites within the oxytocin receptor gene (OXTRm). Through dyadic modeling of behavior and OXTRm status across the first year and a half of life, we translated these findings to 101 human mother-infant dyads. We show that OXTRm is dynamic in infancy and its change is predicted by maternal engagement and reflective of behavioral temperament. We provide evidence for an early window of environmental epigenetic regulation of the oxytocin system, facilitating the emergence of individual differences in human behavior.

Background Chronic shoulder pain (SP) is responsible for significant morbidity, decreased quality of life and impaired work ability, resulting in high socioeconomic burden. Successful SP management is dependent on adherence and compliance with effective evidence-based interventions. Digital solutions may improve accessibility to such treatments, increasing convenience, while reducing healthcare-related costs. Purpose Present the results of a fully remote digital care program (DCP) for chronic SP. Patients and Methods Interventional, single-arm, cohort study of individuals with chronic SP applying for a digital care program. Primary outcome was the mean change between baseline and 12 weeks on the Quick Disabilities of the Arm, Shoulder and Hand (QuickDASH) questionnaire. Secondary outcomes were change in pain (NPRS), analgesic consumption, intention to undergo surgery, anxiety (GAD-7), depression (PHQ-9), fear-avoidance beliefs (FABQ-PA), work productivity (WPAI) and engagement. Results From 296 patients at program start, 234 (79.1%) completed the intervention. Changes in QuickDASH between baseline and end-of-program were both statistically (p < 0.001) and clinically significant, with a mean reduction of 51.6% (mean −13.45 points, 95% CI: 11.99; 14.92). Marked reductions were also observed in all secondary outcomes: 54.8% in NPRS, 44.1% ceased analgesics consumption, 55.5% in surgery intent, 37.7% in FABQ-PA, 50.3% in anxiety, 63.6% in depression and 66.5% in WPAI overall. Higher engagement was associated with higher improvements in disability. Mean patient satisfaction score was 8.7/10.0 (SD 1.6). Conclusion This is the first real-world cohort study reporting the results of a multimodal remote digital approach for chronic SP rehabilitation. High completion and engagement rates were observed, which were associated with clinically significant improvement in all health-related outcomes, as well as marked productivity recovery. These promising results support the potential of digital modalities to address the global burden of chronic musculoskeletal pain.