Avoid Cohen's 'Small', 'Medium', and 'Large' for Power Analysis

Racial bias in pediatric pain perception

Racial disparities in pediatric pain care are prevalent across a variety of healthcare settings, and likely contribute to broader disparities in health, morbidity, and mortality. The present research expands on prior work demonstrating potential perceptual contributions to pain care disparities in adults and tests whether racial bias in pain perception extends to child targets. We examined the perception and hypothetical treatment of pain in Black and White boys (Experiment 1), Black and White boys and girls (Experiment 2), Black and White boys and adult men (Experiment 3), and Black, White, Asian, and Latinx boys (Experiment 4). Across this work, pain was less readily perceived on Black (versus White) boys' faces-though this bias was not observed within girls. Moreover, this perceptual bias was comparable in magnitude to the same bias measured with adult targets and consistently predicted bias in hypothetical treatment. Notably, bias was not limited to Black targets-pain on Hispanic/Latinx boys' faces was also relatively underperceived. Taken together, these results offer strong evidence for racial bias in pediatric pain perception. PERSPECTIVE: This article demonstrates perceptual contributions to racial bias in pediatric pain recognition. Participants consistently saw pain less readily on Black boys' faces, compared to White boys, and this perceptual bias consistently predicted race-based gaps in treatment. This work reveals a novel factor that may support pediatric pain care disparities.

Risk of using smartphones while walking for digital natives in realistic environments: Effects of cognitive-motor interference

The effect of using smartphones while walking on the cognitive and physical abilities of the "digital native" generation, i.e., individuals who have grown up in a digital media-centric environment, remains poorly understood. This study evaluated the effects of cognitive-motor interference on the use of smartphones while walking in children and young adults. The study involved 50 individuals from the digital age generation, including 24 children and 26 young adults. The study encompassed three experimental conditions, in which participants were instructed to traverse a distance of 60 m. The initial condition functioned as a control, wherein the participants walked without supplementary stimuli. In the second condition, the participants were provided with explicit instructions to grasp the smartphone device and position it in front of their chest by using both hands. This manipulation introduced a postural component into the experimental setup. The third condition required participants to be ambulatory while concurrently engaging in a cognitive task, namely, participating in a game that necessitated focused attention. Gait parameters were obtained by using inertial measurement unit sensors. Subsequently, the acquired gait characteristics were converted into dual-task costs (DTC). In the cognitive condition, children exhibited significantly greater DTC values for gait speed (76%), stride length (79%), stride time (102%), and stride length coefficient of variation (CV) than the young adults (p < 0.025). Moreover, as shown by the increased CV, a significant association exists between poor performance in smartphone games among children and increased variability in stride length. In children, the DTC of stride time CV decreased as smartphone game scores increased (R2 = 16.5%), and the DTC of stride length CV decreased more markedly as smartphone game scores increased (R2 = 28.2%). In conclusion, children are at a higher risk of pedestrian accidents when using smartphones while walking compared to young adults.

Single point motion kinematics convey emotional signals in children and adults

This study investigates whether humans recognize different emotions conveyed only by the kinematics of a single moving geometrical shape and how this competence unfolds during development, from childhood to adulthood. To this aim, animations in which a shape moved according to happy, fearful, or neutral cartoons were shown, in a forced-choice paradigm, to 7- and 10-year-old children and adults. Accuracy and response times were recorded, and the movement of the mouse while the participants selected a response was tracked. Results showed that 10-year-old children and adults recognize happiness and fear when conveyed solely by different kinematics, with an advantage for fearful stimuli. Fearful stimuli were also accurately identified at 7-year-olds, together with neutral stimuli, while, at this age, the accuracy for happiness was not significantly different than chance. Overall, results demonstrates that emotions can be identified by a single point motion alone during both childhood and adulthood. Moreover, motion contributes in various measures to the comprehension of emotions, with fear recognized earlier in development and more readily even later on, when all emotions are accurately labeled.

Proposed Specifiers for Conduct Disorder (PSCD) scale: A Latent Profile Analysis with At-Risk Adolescents

The Proposed Specifiers for Conduct Disorder (PSCD; Salekin in Pers Disord: Theory Res Treat 7:180-191, 2016) scale was designed to assess interrelated psychopathic trait domains in conjunction with symptoms of Conduct Disorder (CD) in children and adolescents (i.e., grandiose-manipulative, callous-unemotional, daring-impulsive). Variable-centered studies have provided support for a four-factor PSCD structure (Salekin et al. in Psychol Assess 34(10):985-992, 2022) in line with other adolescent and adult studies. The current person-centered study used latent profile analysis of the PSCD domains to examine whether theoretically meaningful and empirically robust PSCD subtypes emerged from a diverse sample (70.9% White, 20.1% Black, 3.6% Hispanic, and 5.4% other) of adolescents (modal age = 17) in a military style residential facility (N = 409; Males = 80.6%). As hypothesized, a four-class solution was best, consistent with adult psychopathy subtyping research (Hare et al. in Handbook of Psychopathy 39-79, 2018; Roy et al. in Pers Disord: Theory Res Treat, in press). The PSCD subtype profiles were uniform across sex and race/ethnicity. Adolescents evincing a psychopathic trait propensity profile (elevated on all four PSCD domains) displayed the greatest number of arrests and higher overall externalizing psychopathology, compared to the other three latent classes, as well as higher internalizing psychopathology compared to adolescents with general delinquency. The PSCD provides a sound measure of psychopathic trait propensities in youth and our results offer investigators and clinicians a means for understanding person-centered psychopathic traits versus antisocial profiles among at-risk adolescents. Taken together, the current results may offer a viable approach for examining specific treatment targets based on PSCD subtype profiles.

Towards a better understanding of real-world home-visiting programs: a large-scale effectiveness study of parenting mechanisms in Brazil

BACKGROUND

The scale-up of parenting programmes to support early childhood development (ECD) is poorly understood. Little is known about how and when early interventions are most effective. Sustainability of ECD programming requires a better understanding of the mechanisms of real-world interventions. We examined the effects on caregiving practices of Primeira Infância Melhor (PIM), a state-wide home-visiting programme in Brazil.

METHODS

This propensity score matched, longitudinal, quasiexperimental study uses data from the 2015 Pelotas Birth Cohort. We matched children who received PIM at any age with other cohort children on 25 key covariates. Sensitivity, guidance and responsiveness were assessed using video-recorded play tasks. Coerciveness and the parent-child relationship were assessed using the Parenting and Family Adjustment Scales. All parenting outcomes were examined at age 4 years. Separate moderation analyses were conducted for each effect modifier: family income, child age and duration of participation.

RESULTS

Out of 4275 children in the cohort, 797 were enrolled in PIM up to age 4 years. 3018 children (70.6%) were included in the analytic sample, of whom 587 received PIM and 2431 were potential controls. We found a positive effect of PIM on responsiveness (β=0.08, 95% CIs 0.002 to 0.16) and sensitivity (β=0.10, 95% CIs 0.02 to 0.19). No effect was found for any secondary outcomes. Moderation analyses revealed a stronger positive effect on sensitivity for low-income parents (β=0.18, 95% CIs 0.03 to 0.34).

CONCLUSION

A state-wide, home-visiting programme in Brazil improved aspects of responsive caregiving. Effects were more pronounced for low-income families, suggesting benefits of purposeful targeting.

Sample size in quantitative instrument-based studies published in Scopus up to 2022: An artificial intelligence aided systematic review

Despite their popularity, quantitative instruments like Likert scales struggle with a practical issue for research projects - how many participants have to fill out the instrument? This study started with the data for 31,271 articles downloaded from Scopus and, after exclusions, reviewed the sample size used in 21,506 studies. Scimago highest quartile data was brought in for 1999-2021 and linked to the exported articles. Anthropic's Claude and Claude-Instant AI tools were used to analyze the journal article abstracts and extract the sample size information. Frequency distribution of sample size used are presented. Descriptive statistics such as maximum, average and trimmed averaged sample size values are presented by quartile of the journal where the article was published, range of years when the article was published, 3 common analyses which tend to increase sample size, and two population groups which have been shown to impact sample size. The study concludes with ranges for commonly used sample sizes based on a number of criteria. This is one of the first studies to use AI tools to assist in the analysis for a systematic review study.

Acute pain drives different effects on local and global cortical excitability in motor and prefrontal areas: insights into interregional and interpersonal differences in pain processing

Pain-related depression of corticomotor excitability has been explored using transcranial magnetic stimulation-elicited motor-evoked potentials. Transcranial magnetic stimulation-electroencephalography now enables non-motor area cortical excitability assessments, offering novel insights into cortical excitability changes during pain states. Here, pain-related cortical excitability changes were explored in the dorsolateral prefrontal cortex and primary motor cortex (M1). Cortical excitability was recorded in 24 healthy participants before (Baseline), during painful heat (Acute Pain), and non-noxious warm (Warm) stimulation at the right forearm in a randomized sequence, followed by a pain-free stimulation measurement. Local cortical excitability was assessed as the peak-to-peak amplitude of early transcranial magnetic stimulation evoked potential, whereas global-mean field power measured the global excitability. Relative to the Baseline, Acute Pain decreased the peak-to-peak amplitude in M1 and dorsolateral prefrontal cortex compared with Warm (both P < 0.05). A reduced global-mean field power was only found in M1 during Acute Pain compared with Warm (P = 0.003). Participants with the largest reduction in local cortical excitability under Acute Pain showed a negative correlation between dorsolateral prefrontal cortex and M1 local cortical excitability (P = 0.006). Acute experimental pain drove differential pain-related effects on local and global cortical excitability changes in motor and non-motor areas at a group level while also revealing different interindividual patterns of cortical excitability changes, which can be explored when designing personalized treatment plans.

A tutorial on using the paired t test for power calculations in repeated measures ANOVA with interactions

The a priori calculation of statistical power has become common practice in behavioral and social sciences to calculate the necessary sample size for detecting an expected effect size with a certain probability (i.e., power). In multi-factorial repeated measures ANOVA, these calculations can sometimes be cumbersome, especially for higher-order interactions. For designs that only involve factors with two levels each, the paired t test can be used for power calculations, but some pitfalls need to be avoided. In this tutorial, we provide practical advice on how to express main and interaction effects in repeated measures ANOVA as single difference variables. In particular, we demonstrate how to calculate the effect size Cohen's d of this difference variable either based on means, variances, and covariances of conditions or by transforming [Formula: see text] or [Formula: see text] from the ANOVA framework into d. With the effect size correctly specified, we then show how to use the t test for sample size considerations by means of an empirical example. The relevant R code is provided in an online repository for all example calculations covered in this article.

Local brain oscillations and interregional connectivity differentially serve sensory and expectation effects on pain

Pain emerges from the integration of sensory information about threats and contextual information such as an individual's expectations. However, how sensory and contextual effects on pain are served by the brain is not fully understood so far. To address this question, we applied brief painful stimuli to 40 healthy human participants and independently varied stimulus intensity and expectations. Concurrently, we recorded electroencephalography. We assessed local oscillatory brain activity and interregional functional connectivity in a network of six brain regions playing key roles in the processing of pain. We found that sensory information predominantly influenced local brain oscillations. In contrast, expectations exclusively influenced interregional connectivity. Specifically, expectations altered connectivity at alpha (8 to 12 hertz) frequencies from prefrontal to somatosensory cortex. Moreover, discrepancies between sensory information and expectations, i.e., prediction errors, influenced connectivity at gamma (60 to 100 hertz) frequencies. These findings reveal how fundamentally different brain mechanisms serve sensory and contextual effects on pain.

Publication bias impacts on effect size, statistical power, and magnitude (Type M) and sign (Type S) errors in ecology and evolutionary biology

Collaborative efforts to directly replicate empirical studies in the medical and social sciences have revealed alarmingly low rates of replicability, a phenomenon dubbed the 'replication crisis'. Poor replicability has spurred cultural changes targeted at improving reliability in these disciplines. Given the absence of equivalent replication projects in ecology and evolutionary biology, two inter-related indicators offer the opportunity to retrospectively assess replicability: publication bias and statistical power. This registered report assesses the prevalence and severity of small-study (i.e., smaller studies reporting larger effect sizes) and decline effects (i.e., effect sizes decreasing over time) across ecology and evolutionary biology using 87 meta-analyses comprising 4,250 primary studies and 17,638 effect sizes. Further, we estimate how publication bias might distort the estimation of effect sizes, statistical power, and errors in magnitude (Type M or exaggeration ratio) and sign (Type S). We show strong evidence for the pervasiveness of both small-study and decline effects in ecology and evolution. There was widespread prevalence of publication bias that resulted in meta-analytic means being over-estimated by (at least) 0.12 standard deviations. The prevalence of publication bias distorted confidence in meta-analytic results, with 66% of initially statistically significant meta-analytic means becoming non-significant after correcting for publication bias. Ecological and evolutionary studies consistently had low statistical power (15%) with a 4-fold exaggeration of effects on average (Type M error rates = 4.4). Notably, publication bias reduced power from 23% to 15% and increased type M error rates from 2.7 to 4.4 because it creates a non-random sample of effect size evidence. The sign errors of effect sizes (Type S error) increased from 5% to 8% because of publication bias. Our research provides clear evidence that many published ecological and evolutionary findings are inflated. Our results highlight the importance of designing high-power empirical studies (e.g., via collaborative team science), promoting and encouraging replication studies, testing and correcting for publication bias in meta-analyses, and adopting open and transparent research practices, such as (pre)registration, data- and code-sharing, and transparent reporting.

Systematic errors in the perception of rhythm

One hypothesis for why humans enjoy musical rhythms relates to their prediction of when each beat should occur. The ability to predict the timing of an event is important from an evolutionary perspective. Therefore, our brains have evolved internal mechanisms for processing the progression of time. However, due to inherent noise in neural signals, this prediction is not always accurate. Theoretical considerations of optimal estimates suggest the occurrence of certain systematic errors made by the brain when estimating the timing of beats in rhythms. Here, we tested psychophysically whether these systematic errors exist and if so, how they depend on stimulus parameters. Our experimental data revealed two main types of systematic errors. First, observers perceived the time of the last beat of a rhythmic pattern as happening earlier than actual when the inter-beat interval was short. Second, the perceived time of the last beat was later than the actual when the inter-beat interval was long. The magnitude of these systematic errors fell as the number of beats increased. However, with many beats, the errors due to long inter-beat intervals became more apparent. We propose a Bayesian model for these systematic errors. The model fits these data well, allowing us to offer possible explanations for how these errors occurred. For instance, neural processes possibly contributing to the errors include noisy and temporally asymmetric impulse responses, priors preferring certain time intervals, and better-early-than-late loss functions. We finish this article with brief discussions of both the implications of systematic errors for the appreciation of rhythm and the possible compensation by the brain’s motor system during a musical performance.

How to Evaluate Theory-Based Hypotheses in Meta-Analysis Using an AIC-Type Criterion

Meta-analysis techniques allow researchers to aggregate effect sizes-like standardized mean difference(s), correlation(s), or odds ratio(s)-of different studies. This leads to overall effect-size estimates and their confidence intervals. Additionally, researchers can aim for theory development or theory evaluation. That is, researchers may not only be interested in these overall estimates but also in a specific ordering or size of them, which then reflects a theory. Researchers may have expectations regarding the ordering of standardized mean differences or about the (ranges of) sizes of an odds ratio or Hedges' g. Such theory-based hypotheses most probably contain inequality constraints and can be evaluated with the Akaike's information criterion type (i.e., AIC-type) confirmatory model selection criterion called generalized order-restricted information criterion (GORICA). This paper introduces and illustrates how the GORICA can be applied to meta-analyzed estimates. Additionally, it compares the use of the GORICA to that of classical null hypothesis testing and the AIC, that is, the use of theory-based hypotheses versus null hypotheses. By using the GORICA, researchers from all types of fields (e.g., psychology, sociology, political science, biomedical science, and medicine) can quantify the support for theory-based hypotheses specified a priori. This leads to increased statistical power, because of (i) the use of theory-based hypotheses (cf. one-sided vs. two-sided testing) and (ii) the use of meta-analyzed results (that are based on multiple studies which increase the combined sample size). The quantification of support and the power increase aid in, for instance, evaluating and developing theories and, therewith, developing evidence-based treatments and policy.

Emotional processes, collective behavior, and social movements: A meta-analytic review of collective effervescence outcomes during collective gatherings and demonstrations

In this article, we review the conceptions of Collective Effervescence (CE) –a state of intense shared emotional activation and sense of unison that emerges during instances of collective behavior, like demonstrations, rituals, ceremonies, celebrations, and others– and empirical approaches oriented at measuring it. The first section starts examining Émile Durkheim's classical conception on CE, and then, the integrative one proposed by the sociologist Randall Collins, leading to a multi-faceted experience of synchronization. Then, we analyze the construct as a process emerging in collective encounters when individuals contact with social ideal and values, referring to the classical work of Serge Moscovici as well as those more recent empirical approaches. Third, we consider CE as a set of intense positive emotions linked to processes of group identification, as proposed by authors of the Social Identity Theory tradition. Finally, we describe CE from the perspective of self-transcendence (e.g., emotions, experiences), and propose a unified description of this construct. The second section shows the results of a meta-analytical integration (k = 50, N = 182,738) aimed at analyzing CE's proximal effects or construct validity (i.e., Individual Emotions and Communal Sharing) as well as its association with more distal variables, such as Collective Emotions, Social Integration, Social Values and Beliefs and Empowerment. Results indicate that CE strongly associates with Individual Emotions –in particular, Self-Transcendent Emotions– and Communal Sharing constructs (e.g., Group Identity, Fusion of Identity), providing construct validity. Among the distal effects of CE, it is associated with Collective Positive Emotions, long-term Social Integration (e.g., Ingroup Commitment), Social Values and Beliefs and Empowerment-related variables (e.g., Wellbeing, Collective Efficacy, Collective Self-Esteem). Among the moderation analyses carried out (e.g., study design, CE scale, type of collective gathering), the effects of CE in demonstrations are noticeable, where this variable is a factor that favors other variables that make collective action possible, such as Group Identity (rpooled = 0.52), Collective Efficacy (rpooled = 0.37), Negative and Self-Transcendent Emotions (rpooled = 0.14 and 0.58), and Morality-related beliefs (rpooled = 0.43).

Do the Big Five personality traits interact to predict life outcomes? Systematically testing the prevalence, nature, and effect size of trait-by-trait moderation

Personality researchers have posited multiple ways in which the relations between personality traits and life outcomes may be moderated by other traits, but there are well-known difficulties in reliable detection of such trait-by-trait interaction effects. Estimating the prevalence and magnitude base rates of trait-by-trait interactions would help to assess whether a given study is suited to detect interaction effects. We used the Life Outcomes of Personality Replication Project dataset to estimate the prevalence, nature, and magnitude of trait-by-trait interactions across 81 self-reported life outcomes ( n ≥ 1350 per outcome). Outcome samples were divided into two halves to examine the replicability of observed interaction effects using both traditional and machine learning indices. The study was adequately powered (1 − β ≥ .80) to detect the smallest interaction effects of interest (interactions accounting for a Δ R2 of approximately .01) for 78 of the 81 (96%) outcomes in each of the partitioned samples. Results showed that only 40 interactions (5.33% of the original 750 tests) showed evidence of strong replicability through robustness checks (i.e., demographic covariates, Tobit regression, and ordinal regression). Interactions were also uniformly small in magnitude. Future directions for research on trait-by-trait interactions are discussed.

Have I Got the Power? Analysing and Reporting Statistical Power in HRI

This article presents a discussion of the importance of power analyses, providing an overview of when power analyses should be run in the context of the field of Human-Robot Interaction, as well as some examples of how to perform a power analysis. This work was motivated by the observation that the majority of papers published in the proceedings of recent HRI conferences did not report conducting a power analysis; an observation that has concerning implications for many conclusions drawn by these studies. This work is intended to raise awareness and encourage researchers to conduct power analyses when designing research studies using human participants.

Item-Level Psychometric Analysis of the Psychosocial Processes at Work Scale (PROPSIT) in Workers

The structural attributes and correlates of items have an effect on their composite scores and exploring them strengthens the content validity of a measure adapted to another context. The objective of this study was to evaluate the item properties of a measure of psychosocial work factors (PWFs). Data were collected through a web platform from 188 Peruvian working adults (men = 101, 50.5%) holding various professions and jobs. The instrument was the Psychosocial Processes at Work Scale (PROPSIT), adapted for the Peruvian context. The distributional characteristics, the efficiency of its response options and its correlates with engagement, occupational self-efficacy, general stress and psychological distress (explored with a coefficient of maximum information and another of monotonic association) were analyzed. It was found that the items were asymmetrically distributed, without statistical normality and with a response tendency at low (for psychosocial risk factors (PSRFs)) and medium (favorable psychosocial resources) levels. The number of efficient response options was lower (approximately five options) than the original structure (seven options). The monotonic associations with gender and age were essentially zero and theoretically converged with the external constructs, except for some items related to job demands. The contributions of the results to the content validity of the PROPSIT and the orientation of working hypotheses about PROPSIT item constructs and measures of work effects are discussed.

An empirically based power primer for laboratory aggression research

Recent reviews suggest that, like much of the psychological literature, research studies using laboratory aggression paradigms tend to be underpowered to reliably locate commonly observed effect sizes (e.g., r = ~.10-.20, Cohen's d = ~0.20-0.40). In an effort to counter this trend, we provide a "power primer" that laboratory aggression researchers can use as a resource when planning studies using this methodology. Using simulation-based power analyses and effect size estimates derived from recent literature reviews, we provide sample size recommendations based on type of research question (e.g., main effect vs. two-way vs. three-way interactions) and correlations among predictors. Results highlight the large number of participants that must be recruited to reach acceptable (~80%) power, especially for tests of interactions where the recommended sample sizes far exceed those typically employed in this literature. These discrepancies are so substantial that we urge laboratory aggression researchers to consider a moratorium on tests of three-way interactions. Although our results use estimates from the laboratory aggression literature, we believe they are generalizable to other lines of research using behavioral tasks, as well as psychological science more broadly. We close by offering a series of best practice recommendations and reiterating long-standing calls for attention to statistical power as a basic element of study planning.

Temporal–spectral signaling of sensory information and expectations in the cerebral processing of pain

Pain is not only shaped by sensory information but also by an individual’s expectations. Here, we investigated how commonly analyzed electroencephalography (EEG) responses to pain signal sensory information, expectations, and discrepancies thereof (prediction errors) in the processing of pain. Bayesian analysis confirmed that pain perception was shaped by objective sensory information and expectations. In contrast, EEG responses at different latencies (including the N1, N2, and P2 components) and frequencies (including alpha, beta, and gamma oscillations) were shaped by sensory information but not by expectations. Thus, EEG responses to pain are more involved in signaling sensory information than in signaling expectations or prediction errors. Expectation effects are obviously mediated by other brain mechanisms than the effects of sensory information on pain.

The perception of pain is shaped by somatosensory information about threat. However, pain is also influenced by an individual’s expectations. Such expectations can result in clinically relevant modulations and abnormalities of pain. In the brain, sensory information, expectations (predictions), and discrepancies thereof (prediction errors) are signaled by an extended network of brain areas which generate evoked potentials and oscillatory responses at different latencies and frequencies. However, a comprehensive picture of how evoked and oscillatory brain responses signal sensory information, predictions, and prediction errors in the processing of pain is lacking so far. Here, we therefore applied brief painful stimuli to 48 healthy human participants and independently modulated sensory information (stimulus intensity) and expectations of pain intensity while measuring brain activity using electroencephalography (EEG). Pain ratings confirmed that pain intensity was shaped by both sensory information and expectations. In contrast, Bayesian analyses revealed that stimulus-induced EEG responses at different latencies (the N1, N2, and P2 components) and frequencies (alpha, beta, and gamma oscillations) were shaped by sensory information but not by expectations. Expectations, however, shaped alpha and beta oscillations before the painful stimuli. These findings indicate that commonly analyzed EEG responses to painful stimuli are more involved in signaling sensory information than in signaling expectations or mismatches of sensory information and expectations. Moreover, they indicate that the effects of expectations on pain are served by brain mechanisms which differ from those conveying effects of sensory information on pain.

Low Prevalence of A Priori Power Analyses in Motor Behavior Research

A priori power analyses can ensure studies are unlikely to miss interesting effects. Recent metascience has suggested that kinesiology research may be underpowered and selectively reported. Here, we examined whether power analyses are being used to ensure informative studies in motor behavior. We reviewed every article published in three motor behavior journals between January 2019 and June 2021. Power analyses were reported in 13% of studies (k = 636) that tested a hypothesis. No study targeted the smallest effect size of interest. Most studies with a power analysis relied on estimates from previous experiments, pilot studies, or benchmarks to determine the effect size of interest. Studies without a power analysis reported support for their main hypothesis 85% of the time, while studies with a power analysis found support 76% of the time. The median sample sizes were n = 17.5 without a power analysis and n = 16 with a power analysis, suggesting the typical study design was underpowered for all but the largest plausible effect size. At present, power analyses are not being used to optimize the informativeness of motor behavior research. Adoption of this widely recommended practice may greatly enhance the credibility of the motor behavior literature.

Efficacy of neurostimulation across mental disorders: systematic review and meta-analysis of 208 randomized controlled trials

Non-invasive brain stimulation (NIBS), including transcranial magnetic stimulation (TMS), and transcranial direct current stimulation (tDCS), is a potentially effective treatment strategy for a number of mental conditions. However, no quantitative evidence synthesis of randomized controlled trials (RCTs) of TMS or tDCS using the same criteria including several mental conditions is available. Based on 208 RCTs identified in a systematic review, we conducted a series of random effects meta-analyses to assess the efficacy of NIBS, compared to sham, for core symptoms and cognitive functioning within a broad range of mental conditions. Outcomes included changes in core symptom severity and cognitive functioning from pre- to post-treatment. We found significant positive effects for several outcomes without significant heterogeneity including TMS for symptoms of generalized anxiety disorder (SMD = -1.8 (95% CI: -2.6 to -1), and tDCS for symptoms of substance use disorder (-0.73, -1.00 to -0.46). There was also significant effects for TMS in obsessive-compulsive disorder (-0.66, -0.91 to -0.41) and unipolar depression symptoms (-0.60, -0.78 to -0.42) but with significant heterogeneity. However, subgroup analyses based on stimulation site and number of treatment sessions revealed evidence of positive effects, without significant heterogeneity, for specific TMS stimulation protocols. For neurocognitive outcomes, there was only significant evidence, without significant heterogeneity, for tDCS for improving attention (-0.3, -0.55 to -0.05) and working memory (-0.38, -0.74 to -0.03) in individuals with schizophrenia. We concluded that TMS and tDCS can benefit individuals with a variety of mental conditions, significantly improving clinical dimensions, including cognitive deficits in schizophrenia which are poorly responsive to pharmacotherapy.

Cohen's d and physicians' opinion on effect size: a questionnaire on anemia treatment

A well-known effect size (ES) indicator is Cohen's d. Cohen defined d measures of small, medium, and large ES as 0.2, 0.5, and 0.8, respectively. This approach has been criticized because practical and clinical importance depends on the context of research. The aim of the study was to examine physicians' perception of ES using iron deficiency anemia treatment as an example and observing the effects of pretreatment level and duration of treatment on the magnitude of ES. We prepared a questionnaire describing four different clinical studies: (1) 1 month of treatment of anemia in a group of patients with a mean hemoglobin (Hb) of 10 g/dL; (2) 3 months of treatment at an Hb level of 10 g/dL; (3) 1 month of treatment at an Hb level of 8 g/dL; and (4) 3 months of treatment at an Hb level of 8 g/dL. In each scenario, respondents were required to evaluate six various levels of Hb improvement as being very small, small, medium, large, or very large effect: 0.1 g/dL, 0.3 g/dL, 0.7 g/dL, 1.1 g/dL, 1.7 g/dL, and 2.8 g/dL. The responses of 35 physicians were evaluated. For 10 mg/dL, the Cohen's d for small, medium, and large ES was 0.5, 0.8, and 1.2 respectively, for 1 month of treatment. In terms of 3 months of treatment, the Cohen's d was 0.8, 1.2, and 2, respectively. Two separate pretreatment Hb levels (8 g/dL and 10 g/dL) demonstrated a minor difference. Determination of ES during the planning phase of studies requires thorough evaluation of specific clinical cases. Our results are divergent from the classic Cohen's d values. Additionally, duration of treatment affects ES perception.

Modulating Brain Rhythms of Pain Using Transcranial Alternating Current Stimulation (tACS) - A Sham-Controlled Study in Healthy Human Participants

Chronic pain is a major health care problem. A better mechanistic understanding and new treatment approaches are urgently needed. In the brain, pain has been associated with neural oscillations at alpha and gamma frequencies, which can be targeted using transcranial alternating current stimulation (tACS). Thus, we investigated the potential of tACS to modulate pain and pain-related autonomic activity in an experimental model of chronic pain in 29 healthy participants. In 6 recording sessions, participants completed a tonic heat pain paradigm and simultaneously received tACS over prefrontal or somatosensory cortices at alpha or gamma frequencies or sham tACS. Concurrently, pain ratings and autonomic responses were collected. Using the present setup, tACS did not modulate pain or autonomic responses. Bayesian statistics confirmed a lack of tACS effects in most conditions. The only exception was alpha tACS over somatosensory cortex where evidence was inconclusive. Taken together, we did not find significant tACS effects on tonic experimental pain in healthy humans. Based on our present and previous findings, further studies might apply refined stimulation protocols targeting somatosensory alpha oscillations. TRIAL REGISTRATION: The study protocol was pre-registered at ClinicalTrials.gov (NCT03805854). PERSPECTIVE: Modulating brain oscillations is a promising approach for the treatment of pain. We therefore applied transcranial alternating current stimulation (tACS) to modulate experimental pain in healthy participants. However, tACS did not modulate pain, autonomic responses, or EEG oscillations. These findings help to shape future tACS studies for the treatment of pain.

Powering population health research: Considerations for plausible and actionable effect sizes

Evidence for Action (E4A), a signature program of the Robert Wood Johnson Foundation, funds investigator-initiated research on the impacts of social programs and policies on population health and health inequities. Across thousands of letters of intent and full proposals E4A has received since 2015, one of the most common methodological challenges faced by applicants is selecting realistic effect sizes to inform calculations of power, sample size, and minimum detectable effect (MDE). E4A prioritizes health studies that are both (1) adequately powered to detect effect sizes that may reasonably be expected for the given intervention and (2) likely to achieve intervention effects sizes that, if demonstrated, correspond to actionable evidence for population health stakeholders. However, little guidance exists to inform the selection of effect sizes for population health research proposals. We draw on examples of five rigorously evaluated population health interventions. These examples illustrate considerations for selecting realistic and actionable effect sizes as inputs to calculations of power, sample size and MDE for research proposals to study population health interventions. We show that plausible effects sizes for population health interventions may be smaller than commonly cited guidelines suggest. Effect sizes achieved with population health interventions depend on the characteristics of the intervention, the target population, and the outcomes studied. Population health impact depends on the proportion of the population receiving the intervention. When adequately powered, even studies of interventions with small effect sizes can offer valuable evidence to inform population health if such interventions can be implemented broadly. Demonstrating the effectiveness of such interventions, however, requires large sample sizes.

A Primer for Conducting Experiments in Human–Robot Interaction

We provide guidelines for planning, executing, analyzing, and reporting hypothesis-driven experiments in Human–Robot Interaction (HRI). The intended audience are researchers in the field of HRI who are not trained in empirical research but who are interested in conducting rigorous human-participant studies to support their research. Following the chronological order of research activities and grounded in updated research practices in psychological and behavioral sciences, this primer covers recommended methods and common pitfalls for defining research questions, identifying constructs and hypotheses, choosing appropriate study designs, operationalizing constructs as variables, planning and executing studies, sampling, choosing statistical tools for data analysis, and reporting results.

Collective Effervescence, Self-Transcendence, and Gender Differences in Social Well-Being During 8 March Demonstrations

8 March (8M), now known as International Women's Day, is a day for feminist claims where demonstrations are organized in over 150 countries, with the participation of millions of women all around the world. These demonstrations can be viewed as collective rituals and thus focus attention on the processes that facilitate different psychosocial effects. This work aims to explore the mechanisms (i.e., behavioral and attentional synchrony, perceived emotional synchrony, and positive and transcendent emotions) involved in participation in the demonstrations of 8 March 2020, collective and ritualized feminist actions, and their correlates associated with personal well-being (i.e., affective well-being and beliefs of personal growth) and collective well-being (i.e., social integration variables: situated identity, solidarity and fusion), collective efficacy and collective growth, and behavioral intention to support the fight for women's rights. To this end, a cross-cultural study was conducted with the participation of 2,854 people (age 18-79; M = 30.55; SD = 11.66) from countries in Latin America (Mexico, Chile, Argentina, Brazil, Peru, Colombia, and Ecuador) and Europe (Spain and Portugal), with a retrospective correlational cross-sectional design and a convenience sample. Participants were divided between demonstration participants (n = 1,271; 94.0% female) and non-demonstrators or followers who monitored participants through the media and social networks (n = 1,583; 75.87% female). Compared with non-demonstrators and with males, female and non-binary gender respondents had greater scores in mechanisms and criterion variables. Further random-effects model meta-analyses revealed that the perceived emotional synchrony was consistently associated with more proximal mechanisms, as well as with criterion variables. Finally, sequential moderation analyses showed that proposed mechanisms successfully mediated the effects of participation on every criterion variable. These results indicate that participation in 8M marches and demonstrations can be analyzed through the literature on collective rituals. As such, collective participation implies positive outcomes both individually and collectively, which are further reinforced through key psychological mechanisms, in line with a Durkheimian approach to collective rituals.