Does nature-based social prescription improve mental health outcomes? A systematic review and meta-analysis.

Background: A nature-based social prescription (NBSP) is an approach to improving mental health outcomes that involves prescribing nature-based interventions as complementary or alternative therapy to traditional ones. A variety of advantages are available from NBSP for people looking to enhance their mental well-being. The effect size of the nature-based social prescriptions (NBSPs) has not been thoroughly evaluated by systematic reviews and meta-analyses. Objectives: The current study aimed to analyze existing studies and conduct a meta-analysis to determine the overall effect size of the nature-based social prescriptions (NBSP's) outcomes on mental health. Methods: By choosing the relevant papers from among those that were available, a meta-analysis was carried out in the current study. A systematic search of electronic databases (Pub Med, Web of Science, Scopus, Cochrane Library, Embase, CINAHL, and PsychINFO) was conducted to identify relevant studies. Studies were included if they evaluated the effects of NBSP on mental health outcomes. Effect sizes were calculated using the random effects model. Results: Meta-analysis of interventions statistics shows that CBT (SMD -0.0035; 95% CI: [-0.5090; 0.5020]; Tau^2: 0.1011; Tau: 0.318), digital intervention (SMD -0.3654; 95% CI: [-0.5258; 1.2566]; Tau^2: 0.2976, Tau: 0.5455), music intervention (SMD -2.1281; 95% CI: [-0.4659; 4.7221]; Tau^2: 3.4046; Tau:1.8452), and psychological interventions (SMD -0.8529; 95% CI: [0.3051; 1.4007]; Tau^2: 0.1224; Tau: 0.3499) do not significantly impact. The other interventions [social belongingness, communication training, blue intervention, nature-based education, cognitive behavior group therapy (CBGT), social prescribing coordinator, self-help intervention, participatory, organizational intervention, inpatient services, brief diet, internet-based intervention, prenatal intervention, yoga and meditation, ergonomics training program, yoga nidra intervention, and storytelling] highlighted above are significant. Conclusion: The conclusion of the meta-analysis supports the idea that incorporating nature-based social prescription interventions into mental healthcare plans can effectively complement traditional therapies and improve mental health outcomes. Systematic review registration: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023412458, CRD42023412458.

On the effect heterogeneity of established disease susceptibility loci for Alzheimer's disease across different genetic ancestries.

INTRODUCTION: Genome-wide association studies have identified numerous disease susceptibility loci (DSLs) for Alzheimer's disease (AD). However, only a limited number of studies have investigated the dependence of the genetic effect size of established DSLs on genetic ancestry. METHODS: We utilized the whole genome sequencing data from the Alzheimer's Disease Sequencing Project (ADSP) including 35,569 participants. A total of 25,459 subjects in four distinct populations (African ancestry, non-Hispanic White, admixed Hispanic, and Asian) were analyzed. RESULTS: We found that nine DSLs showed significant heterogeneity across populations. Single nucleotide polymorphism (SNP) rs2075650 in translocase of outer mitochondrial membrane 40 (TOMM40) showed the largest heterogeneity (Cochran's Q = 0.00, I2 = 90.08), followed by other SNPs in apolipoprotein C1 (APOC1) and apolipoprotein E (APOE). Two additional loci, signal-induced proliferation-associated 1 like 2 (SIPA1L2) and solute carrier 24 member 4 (SLC24A4), showed significant heterogeneity across populations. DISCUSSION: We observed substantial heterogeneity for the APOE-harboring 19q13.32 region with TOMM40/APOE/APOC1 genes. The largest risk effect was seen among African Americans, while Asians showed a surprisingly small risk effect.

Effect size varies based on calculation method and may affect interpretation of treatment effect: an illustration using randomised clinical trials in osteoarthritis.

BACKGROUND: To illustrate how (standardised) effect sizes (ES) vary based on calculation method and to provide considerations for improved reporting. METHODS: Data from three trials of tanezumab in subjects with osteoarthritis were analyzed. ES of tanezumab versus comparator for WOMAC Pain (outcome) was defined as least squares difference between means (mixed model for repeated measures analysis) divided by a pooled standard deviation (SD) of outcome scores. Three approaches to computing the SD were evaluated: Baseline (the pooled SD of WOMAC Pain values at baseline [pooled across treatments]); Endpoint (the pooled SD of these values at the time primary endpoints were assessed); and Median (the median pooled SD of these values based on the pooled SDs across available timepoints). Bootstrap analyses were used to compute 95% confidence intervals (CI). RESULTS: ES (95% CI) of tanezumab 2.5 mg based on Baseline, Endpoint, and Median SDs in one study were - 0.416 (- 0.796, - 0.060), - 0.195 (- 0.371, - 0.028), and - 0.196 (- 0.373, - 0.028), respectively; negative values indicate pain improvement. This pattern of ES differences (largest with Baseline SD, smallest with Endpoint SD, Median SD similar to Endpoint SD) was consistent across all studies and doses of tanezumab. CONCLUSION: Differences in ES affect interpretation of treatment effect. Therefore, we advocate clearly reporting individual elements of ES in addition to its overall calculation. This is particularly important when ES estimates are used to determine sample sizes for clinical trials, as larger ES will lead to smaller sample sizes and potentially underpowered studies. TRIAL REGISTRATION: Clinicaltrials.gov NCT02697773, NCT02709486, and NCT02528188.

MUSSEL: Enhanced Bayesian polygenic risk prediction leveraging information across multiple ancestry groups.

Polygenic risk scores (PRSs) are now showing promising predictive performance on a wide variety of complex traits and diseases, but there exists a substantial performance gap across populations. We propose MUSSEL, a method for ancestry-specific polygenic prediction that borrows information in summary statistics from genome-wide association studies (GWASs) across multiple ancestry groups via Bayesian hierarchical modeling and ensemble learning. In our simulation studies and data analyses across four distinct studies, totaling 5.7 million participants with a substantial ancestral diversity, MUSSEL shows promising performance compared to alternatives. For example, MUSSEL has an average gain in prediction R2 across 11 continuous traits of 40.2% and 49.3% compared to PRS-CSx and CT-SLEB, respectively, in the African ancestry population. The best-performing method, however, varies by GWAS sample size, target ancestry, trait architecture, and linkage disequilibrium reference samples; thus, ultimately a combination of methods may be needed to generate the most robust PRSs across diverse populations.

The impact of boarding school on student development in primary and secondary schools: a meta-analysis.

As a long-established model of schooling, the boarding system is commonly practiced in countries around the world. Numerous scholars have conducted a great deal of research on the relationship between the boarding school and student development, but the results of the research are quite divergent. In order to clarify the real effects of boarding school on students' development, this study used meta-analysis to quantify 49 (91 effect sizes) experimental or quasi-experimental studies on related topics at home and abroad. The results find that: (1) Overall, boarding school has no significant predictive effect on student development, with a combined effect size of 0.002 (p > 0.05); (2) Specifically, boarding school has a significant positive predictive effect on students' cognitive development (g = 0.248, p < 0.001), a significant negative predictive effect on students' affective and attitudinal development (g = -0.159, p < 0.05), and no significant predictive effect on students' behavioral development (g = -0.115, p > 0.05) and physical development (g = -0.038, p > 0.05); (3) The relationship between the two is moderated by the school stage and the type of boarding school, but not by the instruments; (4) Compared with primary school students, senior high school students and urban boarding students, the negative predictive effect of boarding system on junior middle school students and rural boarding students is more significant. In addition, there are some limitations in the study, such as the limited number of moderator variables included, the results of the study are easily affected by the quality of the included literature, and the dimensionality of the core variable "student development" is not comprehensive enough. In the future, further validation should be conducted through in-depth longitudinal or experimental studies.

Toward diffusion tensor imaging as a biomarker in neurodegenerative diseases: technical considerations to optimize recordings and data processing.

Neuroimaging biomarkers have shown high potential to map the disease processes in the application to neurodegenerative diseases (NDD), e.g., diffusion tensor imaging (DTI). For DTI, the implementation of a standardized scanning and analysis cascade in clinical trials has potential to be further optimized. Over the last few years, various approaches to improve DTI applications to NDD have been developed. The core issue of this review was to address considerations and limitations of DTI in NDD: we discuss suggestions for improvements of DTI applications to NDD. Based on this technical approach, a set of recommendations was proposed for a standardized DTI scan protocol and an analysis cascade of DTI data pre-and postprocessing and statistical analysis. In summary, considering advantages and limitations of the DTI in NDD we suggest improvements for a standardized framework for a DTI-based protocol to be applied to future imaging studies in NDD, towards the goal to proceed to establish DTI as a biomarker in clinical trials in neurodegeneration.

Combination therapy of Epidermal Growth Factor and Growth Hormone-Releasing Hexapeptide in acute ischemic stroke: a phase I/II non-blinded, randomized clinical trial.

Objective: This study tested the hypothesis that a neuroprotective combined therapy based on epidermal growth factor (EGF) and growth hormone-releasing hexapeptide (GHRP6) could be safe for acute ischemic stroke patients, admitting up to 30% of serious adverse events (SAE) with proven causality. Methods: A multi-centric, randomized, open-label, controlled, phase I-II clinical trial with parallel groups was conducted (July 2017 to January 2018). Patients aged 18-80 years with a computed tomography-confirmed ischemic stroke and less than 12 h from the onset of symptoms were randomly assigned to the study groups I (75 µg rEGF + 3.5 mg GHRP6 i.v., n=10), II (75 µg rEGF + 5 mg GHRP6 i.v., n=10), or III (standard care control, n=16). Combined therapy was given BID for 7 days. The primary endpoint was safety over 6 months. Secondary endpoints included neurological (NIHSS) and functional [Barthel index and modified Rankin scale (mRS)] outcomes. Results: The study population had a mean age of 66 ± 11 years, with 21 men (58.3%), a baseline median NIHSS score of 9 (95% CI: 8-11), and a mean time to treatment of 7.3 ± 2.8 h. Analyses were conducted on an intention-to-treat basis. SAEs were reported in 9 of 16 (56.2%) patients in the control group, 3 of 10 (30%) patients in Group I (odds ratio (OR): 0.33; 95% CI: 0.06-1.78), and 2 of 10 (20%) patients in Group II (OR: 0.19; 95% CI: 0.03-1.22); only two events in one patient in Group I were attributed to the intervention treatment. Compliance with the study hypothesis was greater than 0.90 in each group. Patients treated with EGF + GHRP6 had a favorable neurological and functional evolution at both 90 and 180 days, as evidenced by the inferential analysis of NIHSS, Barthel, and mRS and by their moderate to strong effect size. At 6 months, proportion analysis evidenced a higher survival rate for patients treated with the combined therapy. Ancillary analysis including merged treated groups and utility-weighted mRS also showed a benefit of this combined therapy. Conclusion: EGF + GHRP6 therapy was safe. The functional benefits of treatment in this study supported a Phase III study. Clinical Trial Registration: RPCEC00000214 of the Cuban Public Registry of Clinical Trials, Unique identifier: IG/CIGB-845I/IC/1601.

A meta-analysis on the relationship between media with violence and aggression in Iranian sports.

The present research aimed to conduct a systematic study on violence and aggression in the context of Iranian sports and perform a meta-analysis to investigate the association between the media and violence and aggression in sports. The research encompassed all relevant studies available in scientific databases within Iran (such as Magiran, Seyed, Civilica, Normagz, Humane resource study, and police publications), as well as dissertations from the information and scientific documents database. The selected timeframe for this analysis covered the years 2001 to 2018 in the Iranian context. Through this process, 209 studies related to the subject were identified, out of which 10 studies were included in the meta-analysis based on the research protocol investigating the relationship between media and violence and aggression in sports. Data analysis was performed using SPSS25 and CMA2 software. The results showed several variables played prominent roles in the researches on violence and aggression in sports, including media performance, referees' performance, stadium amenities, law enforcement and security factors, external and internal stadium environment, coach's behavior, social control, family influence, education, socio-economic factors, substance abuse, players' behavior, influence of friends, managerial aspects, and cultural and political factors. Inferential statistics indicated effect size for the relationship between media and violence and aggression, under the fixed model, was determined to be 0.259, and under the random model, it was 0.306, both of which were statistically significant. Consequently, based on the findings from the meta-analysis, a significant direct relationship between media and violence and aggression in sports was established.

The resilience of transplanted seagrass traits encourages detection of restoration success.

Restoration of coastal ecosystems, particularly those dominated by seagrasses, has become a priority to recover the important ecosystem services they provide. However, assessing restoration outcomes as a success or failure remains still difficult, probably due to the unique features of seagrass species and the wide portfolio of practices used on transplanting actions. Here, several traits (maximum leaf length, number of leaves, leaf growth rate per shoot, and leaf elemental carbon and nitrogen contents) of transplanted seagrass Posidonia oceanica were compared to reference meadows in five sites of Western Mediterranean Sea in which restoration were completed in different times. Results have evidenced the resilience of transplanted P. oceanica shoots within a few years since restoration, as traits between treatments changed depending on the elapsed time since settlement. The highlighted stability of the restoration time effect suggests that the recovery of the plants is expected in four years after transplanting.

A Practical Significance Bias in Laypeople's Evaluation of Scientific Findings.

People often rely on scientific findings to help them make decisions-however, failing to report effect magnitudes might lead to a potential bias in assuming findings are practically significant. Across two online studies (Prolific; N = 800), we measured U.S. adults' endorsements of expensive interventions described in media reports that led to effects that were small, large, or of unreported magnitude between groups. Participants who viewed interventions with unreported effect magnitudes were more likely to endorse interventions compared with those who viewed interventions with small effects and were just as likely to endorse interventions as those who viewed interventions with large effects, suggesting a practical significance bias. When effect magnitudes were reported, participants on average adjusted their evaluations accordingly. However, some individuals, such as those with low numeracy skills, were more likely than others to act on small effects, even when explicitly prompted to first consider the meaningfulness of the effect.

What Threshold of Amyloid Reduction Is Necessary to Meaningfully Improve Cognitive Function in Transgenic Alzheimer's Disease Mice?

Background: Amyloid-ß plaques (Aß) are associated with Alzheimer's disease (AD). Pooled assessment of amyloid reduction in transgenic AD mice is critical for expediting anti-amyloid AD therapeutic research. Objective: The mean threshold of Aß reduction necessary to achieve cognitive improvement was measured via pooled assessment (n = 594 mice) of Morris water maze (MWM) escape latency of transgenic AD mice treated with substances intended to reduce Aß via reduction of beta-secretase cleaving enzyme (BACE). Methods: Machine learning and statistical methods identified necessary amyloid reduction levels using mouse data (e.g., APP/PS1, LPS, Tg2576, 3xTg-AD, control, wild type, treated, untreated) curated from 22 published studies. Results: K-means clustering identified 4 clusters that primarily corresponded with level of Aß: untreated transgenic AD control mice, wild type mice, and two clusters of transgenic AD mice treated with BACE inhibitors that had either an average 25% "medium reduction" of Aß or 50% "high reduction" of Aß compared to untreated control. A 25% Aß reduction achieved a 28% cognitive improvement, and a 50% Aß reduction resulted in a significant 32% improvement compared to untreated transgenic mice (p < 0.05). Comparatively, wild type mice had a mean 41% MWM latency improvement over untreated transgenic mice (p < 0.05). BACE reduction had a lesser impact on the ratio of Aß42 to Aß40. Supervised learning with an 80% -20% train-test split confirmed Aß reduction was a key feature for predicting MWM escape latency (R2 = 0.8 to 0.95). Conclusions: Results suggest a 25% reduction in Aß as a meaningful treatment threshold for improving transgenic AD mouse cognition.

Effects of medical interventions on health-related quality of life in chronic disease - systematic review and meta-analysis of the 19 most common diagnoses.

Introduction: The demographic shift leads to a tremendous increase in age-related diseases, which are often chronic. Therefore, a focus of chronic disease management should be set on the maintenance or even improvement of the patients' quality of life (QoL). One indicator to objectively measure QoL is the EQ-5D questionnaire, which was validated in a disease- and world region-specific manner. The aim of this study was to conduct a systematic literature review and meta-analysis on the QoL across the most frequent chronic diseases that utilized the EQ-5D and performed a disease-specific meta-analysis for treatment-dependent QoL improvement. Materials and methods: The most common chronic disease in Germany were identified by their ICD-10 codes, followed by a systematic literature review of these ICD-10 codes and the EQ-5D index values. Finally, out of 10,016 independently -screened studies by two persons, 538 studies were included in the systematic review and 216 studies in the meta-analysis, respectively. Results: We found significant medium to large effect sizes of treatment effects, i.e., effect size >0.5, in musculoskeletal conditions with the exception of fractures, for chronic depression and for stroke. The effect size did not differ significantly from zero for breast and lung cancer and were significantly negative for fractures. Conclusion: Our analysis showed a large variation between baseline and post-treatment scores on the EQ-5D health index, depending on the health condition. We found large gains in health-related quality of life mainly for interventions for musculoskeletal disease. Systematic review registration: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020150936, PROSPERO identifier CRD42020150936.

Bayesian meta-analysis of phase 3 results of aducanumab, lecanemab, donanemab, and high-dose gantenerumab in prodromal and mild Alzheimer's disease.

INTRODUCTION: Phase 3 trials using the anti-amyloid antibodies aducanumab, lecanemab, donanemab, and high-dose gantenerumab in prodromal and mild Alzheimer's disease dementia were heterogeneous in respect to statistical significance of effects. However, heterogeneity of results has not yet directly be quantified. METHODS: We used Bayesian random effects meta-analysis to quantify evidence for or against a treatment effect, and assessed the size of the effect and its heterogeneity. Data were extracted from published studies where available and Web based data reports, assuming a Gaussian data generation process. RESULTS: We found moderate evidence in favor of a treatment effect (Bayes factor = 13.2). The effect was moderate to small with -0.33 (95% credible interval -0.54 to -0.10) points on the Clinical Dementia Rating - Sum of Boxes (CDR-SB) scale. The heterogeneity parameter was low to moderate with 0.21 (0.04 to 0.45) CDR-SB points. DISCUSSION: Heterogeneity across studies was moderate despite some trials reaching statistical significance, while others did not. This suggests that the negative aducanumab and gantenerumab trials are in full agreement with the expected effect sizes.

Meta-Analysis of Studies on the Effects of Digital Therapeutics.

Digital therapeutics (DTx), novel treatment methods that have the potential to surpass traditional approaches such as pills, have received considerable research attention. Various efforts have been made to explore effective treatment methods that actively integrate DTx. This review investigates DTx treatment outcomes comprehensively through a meta-analysis. The analysis-a manual search of studies on "digital therapeutics"-includes DTx studies from January 2017 to October 2022. Hedges' g is used to quantify effect size for fifteen studies analyzed, encompassing eight control groups. Further, a quality assessment is performed using the Bias Risk Assessment Tool. The Hedges' g analysis results provide weighted average effect sizes across the eight control groups, revealing a substantial value of 0.91 (95% CI: 0.62 to 1.20); this signifies a moderate to large effect size. Further refinement, which excludes one study, yields an increased weighted average effect size of 1.13 (95% CI: 0.91 to 1.36). The quality assessment results consistently indicate a low risk of bias across studies. The meta-analysis results indicate that DTx can provide significant pivotal therapeutic impacts and offer a means to personalize treatment approaches and streamline the management of patients' treatment processes.

Frequentist, Bayesian Analysis and Complementary Statistical Tools for Geriatric and Rehabilitation Fields: Are Traditional Null-Hypothesis Significance Testing Methods Sufficient?

Null hypothesis significant testing (NHST) is the dominant statistical approach in the geriatric and rehabilitation fields. However, NHST is routinely misunderstood or misused. In this case, the findings from clinical trials would be taken as evidence of no effect, when in fact, a clinically relevant question may have a "non-significant" p-value. Conversely, findings are considered clinically relevant when significant differences are observed between groups. To assume that p-value is not an exclusive indicator of an association or the existence of an effect, researchers should be encouraged to report other statistical analysis approaches as Bayesian analysis and complementary statistical tools alongside the p-value (eg, effect size, confidence intervals, minimal clinically important difference, and magnitude-based inference) to improve interpretation of the findings of clinical trials by presenting a more efficient and comprehensive analysis. However, the focus on Bayesian analysis and secondary statistical analyses does not mean that NHST is less important. Only that, to observe a real intervention effect, researchers should use a combination of secondary statistical analyses in conjunction with NHST or Bayesian statistical analysis to reveal what p-values cannot show in the geriatric and rehabilitation studies (eg, the clinical importance of 1kg increase in handgrip strength in the intervention group of long-lived older adults compared to a control group). This paper provides potential insights for improving the interpretation of scientific data in rehabilitation and geriatric fields by utilizing Bayesian and secondary statistical analyses to better scrutinize the results of clinical trials where a p-value alone may not be appropriate to determine the efficacy of an intervention.

Redefining effect size interpretations for psychotherapy RCTs in depression.

INTRODUCTION: Effect sizes are often used to interpret the magnitude of a result and in power calculations when planning research studies. However, as effect size interpretations are context-dependent, Jacob Cohen's suggested guidelines for what represents a small, medium, and large effect are unlikely to be suitable for a diverse range of research populations and interventions. Our objective here is to determine empirically-derived effect size thresholds associated with psychotherapy randomized controlled trials (RCTs) in depression by calculating the effect size distribution. METHODS: We extracted effect sizes from 366 RCTs provided by the systematic review of Cuijpers and colleagues (2020) on psychotherapy for depressive disorders across all age groups. The 50th percentile effect size, as this represents a medium effect size, and the 25th (small) and 75th (large) percentile effect sizes were calculated to determine empirically-derived effect size thresholds. RESULTS: After adjusting for publication bias, 0.27, 0.53, and 0.86 represent small, medium, and large effect sizes, respectively, for psychotherapy treatment for depressive disorders. DISCUSSION: The effect size distribution for psychotherapy treatment of depression indicates that observed effect size thresholds are larger than Cohen's suggested effect size thresholds (0.2, 0.5, and 0.8). These results have implications for the interpretation of study effects and the planning of future studies via power analyses, which often use effect size thresholds.

Variability in meta-analysis estimates of continuous outcomes using different standardization and scale-specific re-expression methods.

OBJECTIVES: To explore the impact of using different data standardization and scale-specific re-expression methods (i.e., processes to convert standardized data into scale-specific units) in meta-analyses using standardized mean differences (SMDs). STUDY DESIGN AND SETTING: We used data assessed by the Short Physical Performance Battery and the Barthel Index from a meta-analysis of randomized controlled trials which synthesized evidence of physical activity effectiveness on the functional capacity of hospitalized older adults. We standardized the data using study-specific pooled standard deviations (SDs), an internal, and an external SD references. Bayesian meta-analyses were performed for each method to compare the posterior distributions of the meta-analysis parameters. Posterior estimates were re-expressed into scale-specific units applying different methods established in the Cochrane guidelines. RESULTS: Meta-analysis estimates depend on the used standardization method. Analyses including data standardized using the largest SD reference presented lower estimates with less uncertainty in both scales. The method applied for re-expressing SMDs into scale-specific units impacted in their posterior clinical interpretation. The most similar results across models were obtained when using the same SD reference to standardize and re-express data. CONCLUSION: Different data standardization methods yielded different meta-analysis estimates on the SMD scale. To avoid the introduction of bias, the use of a single scale-specific SD reference to standardize data is recommended and instead of study-specific pooled sample SDs. Meta-analysis software packages may therefore change their default methods to allow this method by a single scale-specific SD. To re-express the SMDs into scale-specific units, we suggest the application of the same SD reference that was used for data standardization.

Examining the normality assumption of a design-comparable effect size in single-case designs.

What Works Clearinghouse (WWC, 2022) recommends a design-comparable effect size (D-CES; i.e., gAB) to gauge an intervention in single-case experimental design (SCED) studies, or to synthesize findings in meta-analysis. So far, no research has examined gAB's performance under non-normal distributions. This study expanded Pustejovsky et al. (2014) to investigate the impact of data distributions, number of cases (m), number of measurements (N), within-case reliability or intra-class correlation (ρ), ratio of variance components (λ), and autocorrelation (ϕ) on gAB in multiple-baseline (MB) design. The performance of gAB was assessed by relative bias (RB), relative bias of variance (RBV), MSE, and coverage rate of 95% CIs (CR). Findings revealed that gAB was unbiased even under non-normal distributions. gAB's variance was generally overestimated, and its 95% CI was over-covered, especially when distributions were normal or nearly normal combined with small m and N. Large imprecision of gAB occurred when m was small and ρ was large. According to the ANOVA results, data distributions contributed to approximately 49% of variance in RB and 25% of variance in both RBV and CR. m and ρ each contributed to 34% of variance in MSE. We recommend gAB for MB studies and meta-analysis with N ≥ 16 and when either (1) data distributions are normal or nearly normal, m = 6, and ρ = 0.6 or 0.8, or (2) data distributions are mildly or moderately non-normal, m ≥ 4, and ρ = 0.2, 0.4, or 0.6. The paper concludes with a discussion of gAB's applicability and design-comparability, and sound reporting practices of ES indices.