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This Committee Report provides methodological, interpretive, and reporting guidance for researchers who use measures of heart rate (HR) and heart rate variability (HRV) in psychophysiological research. We provide brief summaries of best practices in measuring HR and HRV via electrocardiographic and photoplethysmographic signals in laboratory, field (ambulatory), and brain-imaging contexts to address research questions incorporating measures of HR and HRV. The Report emphasizes evidence for the strengths and weaknesses of different recording and derivation methods for measures of HR and HRV. Along with this guidance, the Report reviews what is known about the origin of the heartbeat and its neural control, including factors that produce and influence HRV metrics. The Report concludes with checklists to guide authors in study design and analysis considerations, as well as guidance on the reporting of key methodological details and characteristics of the samples under study. It is expected that rigorous and transparent recording and reporting of HR and HRV measures will strengthen inferences across the many applications of these metrics in psychophysiology. The prior Committee Reports on HR and HRV are several decades old. Since their appearance, technologies for human cardiac and vascular monitoring in laboratory and daily life (i.e., ambulatory) contexts have greatly expanded. This Committee Report was prepared for the Society for Psychophysiological Research to provide updated methodological and interpretive guidance, as well as to summarize best practices for reporting HR and HRV studies in humans.

Variability is one of the most crucial outcomes in human movement studies: variance and standard deviation of various parameters have been reported in numerous studies. However, in many of these studies, the numbers of trials and subjects have been intuitively determined and not justified with statistical considerations. Here, we investigated the impact of the numbers of trials and subjects on statistical power, based on the assumption that results per trial follow a normal distribution, using mathematical analysis and numerical simulation. An inverse-like relationship was observed between the number of trials and subjects required to ensure the statistical power for detecting differences in variance between subject groups or conditions. For instance, assuming a 1.2-times difference in population variance between pre-and post-training sessions as an alternative hypothesis, our simulation demonstrated that combinations of the number of subjects and trials, such as measuring 100 trials from each of 12 subjects under each condition, or measuring 20 trials from each of 60 subjects, can guarantee an 80 % of statistical power. Planning research based on such mathematical considerations will enable meaningful statistical interpretations in studies focusing on movement variability, such as gait studies.

BACKGROUND: Using real working examples, we provide strategies and address challenges in linear and logistic regression to demonstrate best practice guidelines and pitfalls of regression modeling in surgical oncology research.
METHODS: To demonstrate our best practices, we reviewed patients who underwent tissue expander breast reconstruction between 2019 and 2021. We assessed predictive factors that affect BREAST-Q Physical Well-Being of the Chest (PWB-C) scores at 2 weeks with linear regression modeling and overall complications and malrotation with logistic regression modeling. Model fit and performance were assessed.
RESULTS: The 1986 patients were included in the analysis. In linear regression, age [β = 0.18 (95% CI: 0.09, 0.28); p < 0.001], single marital status [β = 2.6 (0.31, 5.0); p = 0.026], and prepectoral pocket dissection [β = 4.6 (2.7, 6.5); p < 0.001] were significantly associated with PWB-C at 2 weeks. For logistic regression, BMI [OR = 1.06 (95% CI: 1.04, 1.08); p < 0.001], age [OR = 1.02 (1.01, 1.03); p = 0.002], bilateral reconstruction [OR = 1.39 (1.09, 1.79); p = 0.009], and prepectoral dissection [OR = 1.53 (1.21, 1.94); p < 0.001] were associated with increased likelihood of a complication.
CONCLUSIONS: We provide focused directives for successful application of regression techniques in surgical oncology research. We encourage researchers to select variables with clinical judgment, confirm appropriate model fitting, and consider clinical plausibility for interpretation when utilizing regression models in their research.

This article discusses issues associated with the design and interpretation of biomarker studies, points to various guidelines and lists points to look out for in assessing studies.

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The inter-relationship between physical activity, sedentary behaviour and sleep (collectively defined as physical behaviours) is of interest to researchers from different fields. Each of these physical behaviours has been investigated in epidemiological studies, yet their codependency and interactions need to be further explored and accounted for in data analysis. Modern accelerometers capture continuous movement through the day, which presents the challenge of how to best use the richness of these data. In recent years, analytical approaches first applied in other scientific fields have been applied to physical behaviour epidemiology (eg, isotemporal substitution models, compositional data analysis, multivariate pattern analysis, functional data analysis and machine learning). A comprehensive description, discussion, and consensus on the strengths and limitations of these analytical approaches will help researchers decide which approach to use in different situations. In this context, a scientific workshop and meeting were held in Granada to discuss: (1) analytical approaches currently used in the scientific literature on physical behaviour, highlighting strengths and limitations, providing practical recommendations on their use and including a decision tree for assisting researchers\' decision-making; and (2) current gaps and future research directions around the analysis and use of accelerometer data. Advances in analytical approaches to accelerometer-determined physical behaviours in epidemiological studies are expected to influence the interpretation of current and future evidence, and ultimately impact on future physical behaviour guidelines.

This paper provides guidance on how to properly analyse data generated from the Home Office fingermark grading scheme. The core of the issue is that it creates ordinal data and should therefore not be analysed using averages. To reduce confusion, it is recommended to label the different degrees of fingermark development as classes rather than numerical scores. Appropriate statistical tests are provided to properly analyse Home Office fingermark grading scheme data, however, not using statistical tests is perfectly acceptable so long as conclusions are worded appropriately and do not exaggerate the significance of the findings. Some guidance is provided on estimating sample size and optimal methods for presenting results.

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First, to establish empirically-based effect size interpretation guidelines for rehabilitation treatment effects. Second, to evaluate statistical power in rehabilitation research.
The Cochrane Database of Systematic Reviews was searched through June 2019.
Meta-analyses included in the Cochrane Database of Systematic Reviews with \"rehabilitation\" as a keyword and clearly evaluated a rehabilitation intervention.
We extracted Cohen\'s d effect sizes and associated sample sizes for treatment and comparison groups. Two independent investigators classified the interventions into 4 categories using the Rehabilitation Treatment Specification System. The 25th, 50th, and 75th percentile values within the effect size distribution were used to establish interpretation guidelines for small, medium, and large effects, respectively. A priori power analyses established sample sizes needed to detect the empirically-based values for small, medium, and large effects. Post-hoc power analyses using median sample sizes revealed whether the \"typical\" rehabilitation study was sufficiently powered to detect the empirically-based values. Post hoc power analyses established the statistical power of each test based on the sample size and reported effect size.
We analyzed 3381 effect sizes extracted from 99 meta-analyses. Interpretation guidelines for small effects ranged from 0.08 to 0.15; medium effects ranged from 0.19 to 0.36; and large effects ranged from 0.41 to 0.67. We present sample sizes needed to detect these values based on a priori power analyses. Post hoc power analyses revealed that a \"typical\" rehabilitation study lacks sufficient power to detect the empirically-based values. Post hoc power analyses using reported sample sizes and effects indicated the studies were underpowered, with median power ranging from 0.14 to 0.23.
This study presented novel and empirically-based interpretation guidelines for small, medium, and large rehabilitation treatment effects. The observed effect size distributions differed across intervention categories, indicating that researchers should use category-specific guidelines. Furthermore, many published rehabilitation studies are underpowered.