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Trial procedures | atrial fibrillation, peripheral artery disease stroke, venous thromboembolism | MYOCARDIAL INFARCTION, CHRONIC OBSTRUCTIVE PULMONARY DISEASE, ATRIAL FIBRILLATION, APPENDIX, CORONARY ARTERY DISEASE | At the dedicated study center, patients received a highly standardized 5-h clinical and medical technical examination from October 2016 to June 2020. Trained and certified medical assistants performed all procedures according to standard operating procedures. Comprehensive phenotyping was performed identically at visit 1 (baseline visit) and after 12 weeks of the intervention (visit 3). In addition, patients received a follow-up visit one week after randomization (visit 2).During the visit at the study center, information on current medication (according to Anatomical Therapeutic Chemical (ATC) classification system), cardiovascular risk factors and comorbidities (e.g., atrial fibrillation, coronary artery disease, chronic obstructive pulmonary disease, myocardial infarction, peripheral artery disease stroke, and venous thromboembolism) was collected through physical examination, computer-assisted interviews, anthropometric and blood pressure measurements, as well as laboratory analysis (see Supplemental Appendix for detailed information). In addition to medical-technical examinations, which were mainly focused on the cardiovascular system, blood and urine samples were taken and subsequently stored at − 80 °C for biobanking.Transthoracic echocardiography was conducted using an iE33 echocardiography system with an S5-1 sector array transducer (Royal Philips Electronics, Amsterdam, The Netherlands). Measurements of cardiac structure and function were taken according to current guideline recommendations [ | PMC10293456 |
Study endpoints | The primary endpoint of the EmDia trial was defined as the change of | PMC10293456 | ||
Statistical analysis | obesity, congestive heart failure | REGRESSION, OBESITY, CONGESTIVE HEART FAILURE, LEFT VENTRICULAR HYPERTROPHY | All randomized subjects who received at least one dose of trial treatment and had at least one available post-baseline assessment of the primary analysis variable were included in the intention-to-treat (ITT) population (primary analysis sample). Continuous variables are presented by mean and standard deviation for normal distributions and by median and interquartile range for skewed distributions. Discrete variables are described by relative and absolute frequencies.To account for potential differences between groups and to increase statistical power, it was pre-specified that the analysis of study endpoints would be performed by linear regression analysis with the study endpoint as the dependent variable and empagliflozin 10 mg/day versus placebo as predictor, adjusting for age, sex, and baseline value of each outcome parameter. The respective estimates provide the difference of the change scores by groups. To assess the robustness of the potential effect of empagliflozin on the primary study endpoint, sensitivity analyses were performed in the per-protocol sample and in clinically relevant subgroups: stratified by preserved and reduced left ventricular ejection fraction (≥ 55% vs. < 55%), NT-proBNP within and outside the reference range (< 125 pg/ml vs. ≥ 125 pg/ml), presence of congestive heart failure, left ventricular hypertrophy, and obesity, but also level of uric acid, eGFR, and HbA1c. Finally, mediation analysis using linear regression was performed to quantify the contribution of changes in selected biomarkers to the effect of empagliflozin on the | PMC10293456 |
Results | PMC10293456 | |||
Effect of empagliflozin compared to placebo on left ventricular diastolic function | The Pre-specified analysis of the effect of empagliflozin 10 mg/day versus placebo on the primary study endpoint Analysis of the short-term effect of empagliflozin on diastolic function after 1 week of intervention indicated a decrease in | PMC10293456 | ||
Sensitivity and mediation analyses for the change in left ventricular diastolic function | In a next step, sensitivity analysis in clinically relevant subgroups has been carried out (Fig. Effect of empagliflozin 10 mg/day vs. placebo on the primary study endpoint after 12 weeks of intervention in clinically-relevant subgroups. Beta-estimates for the effect of empagliflozin 10 mg/day compared to placebo on To decipher the contribution of the systemic effects of empagliflozin on the change in left ventricular diastolic function, a mediation analysis for the effect of 12-week empagliflozin on | PMC10293456 | ||
Discussion | heart failure, diastolic dysfunction, diabetes mellitus | DIASTOLIC DYSFUNCTION, DIABETES MELLITUS, SECONDARY, HEART FAILURE, TYPE 2 DIABETES MELLITUS | The present study investigated the effects of the SGLT2 inhibitor empagliflozin in patients with diastolic dysfunction and type 2 diabetes mellitus. The results indicate a significant improvement in diastolic function as measured by left ventricular The results of the EmDia trial add to the growing body of evidence supporting a positive effect of empagliflozin on cardiovascular health [The transient reduction in systolic and diastolic blood pressure after one week of therapy supports the hypothesis that part, and in particular the early, cardiovascular effects of empagliflozin may be mediated via an improvement in ventricular loading through a reduction in afterload that is likely secondary to the diuretic effects of the drug [The improvement in diastolic function found in the EmDia trial provides new insights relevant in context of the results of the recently published EMPEROR-PRESERVED clinical trial investigating the efficacy and safety of empagliflozin in individuals with heart failure and preserved ejection fraction (defined by a left ventricular ejection fraction > 40%) independent of the presence of diabetes mellitus [ | PMC10293456 |
Strengths and limitations | left ventricular | The major strength of the present study is the well-phenotyped cohort, which was studied in a dedicated study center by trained staff in a highly standardized setting minimizing variability of data assessment at high accuracy and reproducibility. However, several limitations should be noted when interpreting the study results. The assessment of diastolic function was limited to left ventricular | PMC10293456 | |
Conclusions | heart failure, type 2 diabetes mellitus | HEART FAILURE, TYPE 2 DIABETES MELLITUS | The results of the present EmDia trial demonstrated that empagliflozin 10 mg/day improved diastolic function in patients with type 2 diabetes mellitus and elevated left ventricular end-diastolic pressure within 12 weeks of treatment. The beneficial effect of empagliflozin was consistent across all subgroups and also occurred in subjects with heart failure and preserved ejection fraction, supporting the positive effect of empagliflozin reported in the literature regarding the treatment of patients with heart failure and including patients with heart failure with preserved ejection fraction. Because the identified positive hemodynamic, metabolic, and hematological effects of empagliflozin explain only part of its effect on cardiac function, future studies will be important to identify to what extent this mechanism contributes to improved clinical outcome in subjects with heart failure. | PMC10293456 |
Supplementary Information | Below is the link to the electronic supplementary material.Supplementary file1 (DOCX 144 KB) | PMC10293456 | ||
Acknowledgements | We would like to thank all study participants of the EmDia trial for their participation in this clinical study. Our thanks go to the clinical staff of the study center, the Interdisciplinary Center for Clinical Trials Mainz (IZKS) for trial support (i.e., Dr. Kai Kronfeld, Dr. Silke Warnke, Christoph Medler, Dr. Jana Topsch, Anja Powaska, and Dr. Christian Ruckes) and all medical colleagues (i.e., Dr. Ernst Hauser, Dr. Christoph Fleckenstein, Dr. Judith Prochaska, Dr. Madeleine Busch, Wolfgang Reeh, Dr. Michael Drexler, Dr. Susanne Thiel, Dr. Konstantin Katsaros, Dr. Armin Schütz, Dr. Julia Weinmann-Menke, and Dr. Matthias M. Weber) contributing to the successful implementation and conduct of this project. | PMC10293456 | ||
Author contributions | TG, SJS, FM | JHP contributed to study design, collection of data, data analysis, and drafted and revised the manuscript. CJ contributed to study design, supported data analysis and revised the manuscript for important intellectual content. AS contributed to study design, performed statistical analysis, contributed to the interpretation of data and revised the manuscript for important intellectual content. NA contributed to data collection, revised the manuscript and provided critical intellectual input. FM revised the manuscript and provided critical intellectual input. MWH revised the manuscript and provided critical intellectual input. RB revised the manuscript and provided critical intellectual input. DZ contributed to study organization and conduct, revised the manuscript, and provided critical intellectual input. TK contributed to data collection, revised the manuscript, and provided critical intellectual input. SOT contributed to data collection, revised the manuscript, and provided critical intellectual input. KJL contributed to study design and data collection, revised the manuscript, and provided critical intellectual input. A.D. contributed to study design, data collection, revised the manuscript, and provided critical intellectual input. HB contributed to study design, supported statistical analysis, revised the manuscript and provided critical intellectual input. SJS revised the manuscript and provided critical intellectual input. TG contributed to study design, data collection, revised the manuscript and provided critical intellectual input. T.M. contributed to study oversight, revised the manuscript and provided critical intellectual input. PSW acquired funding, contributed to study design, data analysis, and interpretation of data, provided study oversight and performed critical revision of the manuscript for important intellectual content. | PMC10293456 | |
Funding | Open Access funding enabled and organized by Projekt DEAL. The EmDia trial is an academic, GCP sponsor-investigator clinical trial funded with support of Boehringer Ingelheim. Dr. Wild and Dr. Prochaska are funded by the Federal Ministry of Education and Research (BMBF 01EO1503). Dr. Gori, Dr. Münzel and Dr. Wild are principal investigators of the German Center for Cardiovascular Research (DZHK). Dr. Wild is principal investigator of the DIASyM research core (BMBF 161L0217A). | PMC10293456 | ||
Data availability | This project constitutes a major scientific effort with high methodological standards and detailed guidelines for analysis and publication. Data are not made available for the scientific community outside the established and controlled workflows and algorithms. To meet the general idea of verification and reproducibility of scientific findings, we offer access to data at the local database in accordance with the ethics vote on request (contact: Prof. Dr. Philipp Wild (PI), philipp.wild@unimedizin-mainz.de). | PMC10293456 | ||
Declarations | PMC10293456 | |||
Conflict of interest | MyoKardia | EDWARDS | Dr. Prochaska has received honoraria for lectures from Boehringer Ingelheim and Bayer AG outside the topic of the present study. Dr. Wild has received research funding outside the topic of the present study from Boehringer Ingelheim, Sanofi-Aventis, Bayer Healthcare, Daiichi Sankyo Europe, and Novartis, and received outside the topic of the present study honoraria for lectures or consulting from Boehringer Ingelheim, Bayer HealthCare, Evonik, AstraZeneca and Sanofi-Aventis. Dr. Tröbs has received lecture fees for Philips AG outside the submitted work. Dr. Shah has received research grants from Actelion, AstraZeneca, Corvia, Novartis, and Pfizer; and has received consulting fees from Abbott, Actelion, AstraZeneca, Amgen, Axon Therapies, Bayer, Boehringer-Ingelheim, Bristol-Myers Squibb, Cardiora, CVRx, Cytokinetics, Edwards, Eidos, Eisai, Ionis, Ironwood, Lilly, Merck, MyoKardia, Novartis, Novo Nordisk, Pfizer, Prothena, Sanofi, Shifamed, Tenax, and United Therapeutics. | PMC10293456 |
Collaborators of the study | Thomas | Dr. Jürgen H. Prochaska, Dr. Andreas Schulz, Dr. Natalie Arnold, Dr. Felix Müller, Dr. Marc William Heidorn, Rieke Baumkötter, Dr. Daniela Zahn, Dr. Thomas Koeck, Dr. Sven-Oliver Tröbs, Dr. Karl J. Lackner, Dr. Andreas Daiber, Dr. Harald Binder, Dr. Sanjiv J. Shah, Dr. Tommaso Gori, Dr. Thomas Münzel, Dr. Philipp S. Wild, Dr. Steffen Rapp; Interdisciplinary Center for Clinical Trials (IZKS) Mainz (i.e., Dr. Silke Warnke, Dr. Kai Kronfeld, Dr. Christian Ruckes, Christoph Medler). | PMC10293456 | |
References | PMC10293456 | |||
Subject terms | Women have less influence than men in a variety of settings. Does this result from stereotypes that depict women as less capable, or biased interpretations of gender differences in behavior? We present a field experiment that—unbeknownst to the participants—randomized the gender of avatars assigned to Democrats using a social media platform we created to facilitate discussion about the 2020 Primary Election. We find that misrepresenting a man as a woman undermines his influence, but misrepresenting a woman as a man does not increase hers. We demonstrate that men’s higher resistance to being influenced—and gendered word use patterns—both contribute to this outcome. These findings challenge prevailing wisdom that women simply need to behave more like men to overcome gender discrimination and suggest that narrowing the gap will require simultaneous attention to the behavior of people who identify as women and as men. | PMC10462641 | ||
Introduction | female-typed behaviors, incongruity, male-typed behaviors | Women have less influence than men in a variety of decision-making settings such as businessIn this article, we ask: What causes women’s lower influence in discussions about politics, and how might it be improved? Previous research indicates gender gaps emerge in social contexts where people already have expectations of gender norms for a particular domainStudies that explain the gender gap in political influence as the result of differential treatment of men and women speakers emphasize the role of stereotypes—often independent of differences in the actual behavior of men and womenGender performance—or the practices and habits of femininity and masculinity that women and men use to communicate gender—may also contribute to women’s lower influence, because male-typed behaviors are more highly valued in some settings than female-typed behaviors. Studies of gender performance emphasize differences in word choice, tone, or behaviorsWhile these are not competing explanations, the majority of the research just described attempts to isolate and evaluate a single explanatory theory, often using research designs that hold core features of the other theory constant. This leads to an incomplete understanding of the complexity of gender dynamics in interpersonal interactions—particularly in online spaces that allow people greater freedom to control gendered cues in their self-presentation—and the promotion of solutions based on changing other actors’ gender biasesIn this experiment, we randomly paired two people who identified as Democrats to have an online, text-based conversation about the 2020 Presidential Primary Election in the United States. In the control condition, a woman and a man had a conversation together, and each participant was represented by a gendered avatar that was visible only to their conversation partner. In some treatment conversations, we manipulated gender perceptions by randomly varying whether these avatars were consistent or inconsistent with the partner’s self-reported gender identity. In another set of conversations, we paired respondents with a conversation partner of their same gender with a correctly-gendered avatar. This design allows us to examine how perceptions of gender interact with gender performance as both unfold over the course of a conversation.In particular, if women who are mislabeled as men gain relative influence in the conversation, this would be evidence that gendered expectations are a main cause of women’s lower influence in interpersonal settings. Under this explanation, we would also expect little difference in the gendered content of the language used by men and women. By contrast, if women who are mislabeled as men experience no change in their relative influence compared to women who are not mislabeled, this would be evidence that sexism in the response to how women perform their gender is a primary cause of women’s lower influence. This theory also predicts identifiable and relatively static differences in the gendered language used by men and women, regardless of the avatar assigned. However, if both explanations are simultaneously occurring, then role incongruity might lead to a decrease in influence for both men and women when their gender is misrepresented | PMC10462641 | |
Experimental design | APPENDIX | We conducted a field experiment during the 2020 U.S. Democratic presidential primary election on a text-based social media platform designed for academic research,(see Fig.
Research design. In the control condition, one man talks to one woman, and both discussants are represented by an avatar associated with their self-reported gender. The treatment conditions also pair one man and one woman, but the treated discussion partner views an avatar that does not match the self-identified gender of their mislabeled discussion partner. The same-gender conditions pair men to converse with another man and women to converse with another woman, and both discussants are represented by an avatar associated with their self-reported gender. Note that contrasting the same-gender and any cross-gender conversations does not identify the causal effect of gender because only partners were randomized, not partner gender (holding other partner characteristics fixed). Men and women differ on many unobserved characteristics, so changing someone’s discussion partner from a man to a woman changes more than just the partner’s gender. See Supplementary Appendix Sect. In the conversation, gender presentation is manipulated through random assignment of a male or female avatar for some respondents. The text-based conversation means that gendered patterns develop through an ongoing social interaction, but that gender can only be communicated through the display avatar and the text of messages sent, with no interference from physical, visual, or vocal cues.Participants were randomly assigned to one treatment or control conversation, and the experimental effects are estimated by comparing the between-subjects differences in averages using | PMC10462641 | |
Results | We report our results using four types of outcomes. First, we compare the gender gap in the level of influence of each partner in a cross-gender conversation. The gap is evaluated using three metrics, and their composite index: (1) the partner’s subjective survey report of the subject’s influence on their attitudes, (2) the pre-post change in the partner’s thermometer rating of the candidate most preferred by the subject in the pre-survey, (3) the pre-post change in the partner’s ranking of the candidate most preferred by the subject in the pre-survey. The index provides an indication of how much influence a person has in a conversation relative to the influence of their partner, and the average difference between partners provides a metric of the gender gap. Our expectations for the value of this metric for each competing theory are provided in Table Theoretical expectations of influence gap.Second, we look at a conversation-level metric of convergence in the thermometer ratings of the full set of candidates, which shows how attitudes converge or diverge at the conversation level and beyond just the evaluations of the top-ranked candidate. Third, we examine the influence metrics (the same set as the gender gap analysis) at the individual level, to more closely examine the dynamics of who has influence and whose influence is changing in response to the experimental intervention. Finally, we use a dictionary-based evaluation of gendered language to compare the gendered language used by men and women in each type of conversation. | PMC10462641 | ||
Influence gap | POSITIVE | Figure Effects of gender mislabeling on the influence gap in cross-gender conversations about the 2020 Democratic Primary Election. The influence gap measures the difference between the influence of the man and woman on the given measure. Positive values indicate that the man is more influential. Dots are point estimates of the gap with 90% and 95% confidence intervals. Stars indicate significant differences between mislabeled conversations (orange for mislabeled men or green for mislabeled women) and correctly labeled conversations (black) using two-tailed t-tests. One star indicates significance at the 5% level, two indicates significance at the 1% level. In correctly labeled conversations, men are more influential on all metrics—and significantly so for the aggregate index (The results for both treatment conditions are inconsistent with either the gender stereotypes or gender performance theories alone. Rather, when men are mislabeled as women (orange lines), men’s influence is reduced such that the influence gap changes direction, meaning that—relative to their male partners—women are on average the more influential partner when their male partners are misrepresented as women. Women’s influence likewise does not improve when they are misrepresented as men—if anything, women lose relative influence in those conversations compared to the control, but these differences are not statistically significant.These results indicate that, regardless of one’s actual gender, mislabeling someone’s gender reduces (or at least does nothing to improve) their level of political influence relative to their partner. This outcome is consistent with sociological explanations that predict negative effects when people’s actual behaviors contradict stereotyped expectations for how they should behave in a particular setting | PMC10462641 | |
Attitude convergence | To further explore the consequences of the mislabeling intervention for the overall trajectory of conversations, we examine a conversation-level metric of convergence in thermometer rankings across all candidates. We compare the average gap between the subject’s rating of each candidate and their partner’s rating of the same candidate, before and after the conversation. This metric has the advantage of looking at changes in more than just one subject’s top-rated candidate, and it allows for reciprocal influence across the full range of candidates. Same-gender conversations are excluded from this analysis because the initial level of agreement on candidates between conversation partners is different than for cross-gender conversations due to gendered differences in candidate preferences (see the The left panel of Fig. Mislabeling effects on attitude convergence. The left panel shows the magnitude of disagreements in feelings towards candidates pre- and post-conversation by treatment condition and the right panel explicitly shows treatment effects on the within-conversation changes in feelings towards candidates. This visualization depicts the absolute difference in thermometer rating for the same candidate across conversation partners within either the pre- or post-conversation survey. Conversations where the man is mislabeled exhibit increases in disagreement that are significantly larger (shown in the right panel, two-tailed | PMC10462641 | ||
Individual-level influence metrics | Because a conversation is a dynamic interaction between two people, the change in the influence gap metrics might come from two mechanisms—a change in the persuasiveness of one partner, or a change in the other partner’s propensity to be influenced. In order to distinguish these two mechanisms, we evaluate the treatment effects of mislabeling on both the influence exerted by and the propensity to be influenced of each discussion partner separately. Fig. The top panel represents the level of persuasiveness of respondents when the person they are influencing is a woman, whereas the bottom panel represents influence on a male partner. The difference between the top and bottom panels for cross-gender conversations is the influence gap measure presented in panel A of Fig. Contrary to expectations, women do not have less influence in the conversation than men Figure While our initial emphasis was on increasing women’s influence by changing how they themselves are perceived by their male partners, what we discover is that changing how women perceive (and, therefore, treat) their male partner had a bigger impact. This finding is particularly notable because it suggests that men are not inherently or immutably less persuadable than women—they are only less persuadable when their partners recognize them as men. In the experimental condition where women do not realize they are talking to a man, women are able to exert more influence on their partner. Put another way, men’s attitudes are more malleable when they are not treated like men, which suggests that the assumptions and behavioral decisions that discussion partners make when interacting with men reinforce the influence gap, possibly even more than the assumptions and behavioral decisions that people make when interacting with women.Average influence on partners by partner’s gender and treatment condition. Point estimates and error bars indicate average level of influence exerted by the subject. Influence is measured with the aggregate influence index (panel A in Fig. The individual-level analysis provides evidence that both gendered behavior and gendered perceptions interact in complex ways within a dynamic conversation. Although only one partner is treated (views the mis-assigned gendered avatar for their partner), the treatment has effects on both parties in the conversation. Because their perceptions are not directly manipulated, the only way effects on the mislabeled partner are possible is if their partner changes their language or conversational style in response to the gendered avatar of their partner, and they respond in kind. We examine this explanation in the next section. | PMC10462641 | ||
Language choice | Thus far, we have provided evidence consistent with a theory that both gender stereotypes and gender performance interact to produce gender inequality in interpersonal influence, but that neither gender stereotypes nor gendered performance are a dominant mechanism for that effect. We next turn to a direct evaluation of the gendered language used in the text of the conversations. Because written text is the only communication between respondents on our platform, variation in gendered performances can only be the result of different patterns of language use in the text exchanges. Furthermore, because the mislabeled partner does not know their gender has been misrepresented, changes in their language use can only arise as a reaction to the language used by their partner who has stereotyped expectations of their gender.Complementary to the theoretical expectations of influence presented in Table We use a dictionary of gendered political words developed by Roberts and UtychFigure Patterns of gendered language usage in chats on the social media platform by treatment condition. X axis describes average gender connotation of words, based on the Roberts and Utych dictionary databaseHowever, when one of the discussion partners is mislabeled, we find it changes both men’s We believe this demonstrates the complex, emergent nature of language and gender discrimination in real life settings. The results of all four analyses show evidence that both stereotypes and behaviors are at play, and that the interaction of both produces something distinctive from either. Importantly, our findings further highlight the limitations of interventions that are non-interactive or limited to a single exchange. Gender for both participants is constructed and reinforced continually throughout the course of a dynamic interaction | PMC10462641 | ||
Discussion | incongruity | Gender gaps in interpersonal influence are far more complex than many theories account for. Using a field experiment on an anonymous chat platform created to simulate social media conversations during the 2020 presidential primaries, we randomized people to talk with partners of different genders while being represented by avatars that were either consistent or inconsistent with their self-identified gender. This design allowed us to study how expectations of lower political competence among women (gender stereotypes) and differences in the actual text of language used by men and women (gendered performance) interact to create gender inequality in interpersonal political influence. A gender stereotype explanation would have predicted that mislabeling women as men would improve their influence in the conversation, whereas men’s influence would be lower when they were mislabeled as women. By contrast, a gendered behavior explanation would have expected no impact of mislabeling on the persuasiveness of the mislabeled individual. What we found is not consistent with either hypothesis, and instead points to the interaction of both mechanisms via role incongruity.Using multiple metrics of influence in the conversation, we find evidence of a clear gender gap in influence in cross-gender control conversations. However, when women in the conversation are mislabeled as men, their influence does not improve, and instead may actually decrease. Likewise, mislabeling men as women reduces men’s influence and may even reverse the gender gap in influence. We use individual-level metrics of influence, accounting for the partner’s gender, and find that the gender gap seems to exist more because of the perception of the partner’s propensity to be influenced than any gender difference in the subject’s persuasiveness.Men, in general, are much less likely to Additionally, we find gender differences in language use, and evidence that people change their language in response to both the perceived and actual gender and language use of the other person. Men and women use distinct vocabularies in political conversations, which communicate their gender to others. Additionally, men and—especially—women seem to adapt their behavior in response to the perceived gender of the person with whom they are talking. This suggests that performances of gender in political conversation are relational, constructed in response to both stereotypes attached to a conversation partner’s gender presentation and observations of their gender performance.Thus, the complex and dynamic interpersonal construction of gender cannot be easily or durably manipulated using a single, static intervention. Proposed practical solutions and future research on women’s influence must take into account a more complex model than can be achieved by relying on just one of these explanations while holding other features constant. Allowing for this complexity in research designs is particularly important when studying conversation in online spaces, where gender cues are often more easily controlled and more often misinterpreted than in face-to-face discussion. Unfortunately, this also implies that the solutions to improving women’s influence are inherently difficult. Women cannot improve their levels of influence simply by talking more like men or “leaning in.” But neither can women become more influential without accounting for different perceptions of the persuasiveness and persuadability of both men and women.There are several important limitations to this study. First, the discussions are limited to the realm of American politics, which is a highly gendered domain for interpersonal interactionFurthermore, we only looked at Democrats. Gender in politics functions differently across partiesAlso, we considered a single, uniquely gendered primary election cycle. It is possible that our results were affected by gendered differences in candidate support and enthusiasm. In Supplemental Materials Sect. Additionally, our sample is relatively White, highly educated, and excludes people who identify outside the gender binary. When combined with the “normative” nature of these identities in unmarked situationsFinally, while we provide evidence that gender is communicated and constructed between partners using language, future research should further investigate those dynamics. Additionally, gender is performed in many ways that go beyond language. In in-person interactions, differences in vocal tone, appearance, or body language may additionally contribute to gender differences | PMC10462641 | |
Methods | RECRUITMENT, APPENDIX | We hired the survey firm YouGov to recruit self-identified Democrats who were told they had been randomly selected for an opportunity to earn $10 for testing a new app. Recruitment started on February 28, 2020, and the app, called UniteDem, was described as a way for Democrats to anonymously discuss which candidate was best positioned to defeat Donald Trump in the general election. Respondents were asked to install UniteDem on an iOS or Android mobile device and given an invite code that we used to assign them to one of several treatment conditions described below. Figure
Screenshots of the Social Media Platform Used in Study. 596 Democrats were recruited to download an app. They completed a survey about their views about presidential candidates and were instructed to discuss which one was best positioned to defeat Trump in the 2020 Democratic Primary election. After completing the in-app survey, some respondents were randomly assigned an avatar (seen only by their discussion partner) that was inconsistent with their self-reported gender. After a 14 exchange chat with another Democrat in the study, respondents completed a post-survey of their attitudes.Next, respondents were redirected to a screen that presented a brief video in which a series of male and female silhouettes circled around while informing the user that the app was searching for a discussion partner (see Fig. Conversations ended after the participants had completed 14 exchanges, where an “exchange” is defined as one user sending one message or a few successive messages followed by one or more successive messages from the other user, or at 7 pm eastern standard time on March 3, 2020 (Super Tuesday) if they had not yet completed all exchanges. All conversations where both participants completed the post survey before the Super Tuesday deadline are included in the analysis; our results are robust to excluding the eleven conversations with fewer than 14 exchanges, see Supplemental Appendix Sect. Following the conversation, the app asked respondents (N = 596; attrition is addressed in the All of our research was approved by the Institutional Review Board at Duke University. Respondents provided informed consent to participate and were debriefed about the nature of the study and experimental manipulation after the conclusion of the experiment. All methods were performed in accordance with the relevant guidelines and regulations. | PMC10462641 | |
Supplementary Information | The online version contains supplementary material available at 10.1038/s41598-023-39359-0. | PMC10462641 | ||
Acknowledgements | BROWN | The authors are grateful to Jessica Preece, Chris Karpowitz, Lynn Smith-Lovin, Diana O’Brien, Craig Rawlings, Ashley Harrell, Taylor Brown, members of the Workshop on American Politics at the University of North Carolina Chapel Hill, the PoNE Lab Workshop at Aarhus University, and the BYU Thursday Group for comments and suggestions about how to improve this research. | PMC10462641 | |
Author contributions | Conceptualization: A.C., G.T., F.A., D.C., G.V., C.B., A.V. Methodology: A.C., G.T., F.A., D.C., G.V., C.B., A.V. Software: A.C. Investigation: A.C., G.T., F.A., D.C., G.V., C.B., A.V. Formal analysis: A.C., G.T., F.A., A.C.A., L.A., A.V. Writing—Original Draft: A.C., G.T., F.A., A.C.A., L.A., C.B. Writing—Review and Editing: A.C., G.T., G.V., A.C.A., L.A., C.B., A.V. Visualization: A.C., G.T., F.A., A.C.A. Supervision: C.B. Project administration: C.B., A.V., A.C., G.T., F.A., D.C., G.V. Funding acquisition: C.B., A.V. | PMC10462641 | ||
Funding | Funding for this project was generously provided by the Duke University Provost and the National Science Foundation (DMS-2046880). | PMC10462641 | ||
Data availability | Anonymized replication code and data are available at this link: | PMC10462641 | ||
Code availability | Upon publication, replication code will be made publicly available by the authors at this link: | PMC10462641 | ||
Competing interests | The authors declare no competing interests. | PMC10462641 | ||
References | PMC10462641 | |||
Context | PATHOPHYSIOLOGY | Edited by: Roberta Minelli, University Hospital of Parma, ItalyReviewed by: Matteo Spaziani, Division of Medical Pathophysiology, Food Science and Endocrinology, Sapienza University of Rome, Italy; Joanne Rovet, University of Toronto, Canada†ORCID: Divya M. Mathews, This article was submitted to Pediatric Endocrinology, a section of the journal Frontiers in EndocrinologyHysterosalpingography (HSG) using oil-soluble contrast medium (OSCM) improves pregnancy rates but results in severe and persistent iodine excess, potentially impacting the fetus and neonate. | PMC9927197 | |
Objective | thyroid dysfunction | THYROID DYSFUNCTION | To determine the incidence of thyroid dysfunction in newborns conceived within six months of OSCM HSG. | PMC9927197 |
Design | Offspring study of a prospective cohort of women who underwent OSCM HSG. | PMC9927197 | ||
Setting | Auckland region, New Zealand (2020-2022) | PMC9927197 | ||
Participants | Offspring from the SELFI (Safety and Efficacy of Lipiodol in Fertility Investigations) study cohort (n=57). | PMC9927197 | ||
Measurements | All newborns had a dried blood spot card for TSH measurement 48 hours after birth as part of New Zealand’s Newborn Metabolic Screening Programme. Forty-one neonates also had a heel prick serum sample at one week to measure thyroid-stimulating hormone (TSH), free thyroxine (FT4), and free triiodothyronine (FT3). Maternal urine iodine concentration (UIC) and TSH in the six months after OSCM HSG were retrieved from the SELFI study for analyses. | PMC9927197 | ||
Primary outcome | hypothyroidism | HYPOTHYROIDISM | Incidence of hypothyroidism in the neonatal period. | PMC9927197 |
Results | primary hypothyroidism | PRIMARY HYPOTHYROIDISM | There was no evidence of primary hypothyroidism on newborn screening (TSH 2-10 mIU/L). All neonates tested at one week had normal serum TSH, FT4, and FT3 levels. However, increasing maternal peak UIC levels during pregnancy were associated with lower TSH levels (p= 0.006), although also associated with lower FT4 levels (p=0.032). | PMC9927197 |
Conclusions | NEONATAL HYPOTHYROIDISM | While pre-conceptional OSCM HSG in women did not result in neonatal hypothyroidism, gestational iodine excess was associated with a paradoxical lowering of neonatal TSH levels despite lower FT4 levels. These changes likely reflect alterations in deiodinase activity in the fetal hypothalamic-pituitary axis from iodine excess. | PMC9927197 | |
Introduction | PRIMARY HYPOTHYROIDISM | Oil-soluble contrast medium (OSCM) hysterosalpingography (HSG) improves pregnancy rates in women under 40 years of age (As OSCM HSG is becoming increasingly popular as a fertility-enhancing procedure, it is essential to establish its potential effects on neonatal thyroid function. Previous studies examining thyroid function in newborns conceived following an OSCM HSG had contradictory findings, with one showing an increased risk of primary hypothyroidism and the others showing no increased risk ( | PMC9927197 | |
Aims and objectives | CONGENITAL HYPOTHYROIDISM, SUBCLINICAL HYPOTHYROIDISM | This study aimed to prospectively determine the thyroid function status of the newborns conceived following OSCM HSG, and establish whether there were any associations between maternal iodine or thyroid hormone levels following the HSG and neonatal thyroid function. The objectives were to determine the incidence of:Subclinical hypothyroidism in the newborn, defined as either: a) mild TSH elevation in the newborn screening (TSH 10–15 mIU/L) from dried blood spot cards (Congenital hypothyroidism, defined as TSH >15 mIU/L from dried blood spot cards in the newborn screening and persistently elevated serum TSH levels or reduced FT4 in subsequent tests, requiring ongoing thyroxine replacement based on the stepwise complex protocol for diagnosis ( | PMC9927197 | |
Materials and methods | thyroid dysfunction, hypothyroidism | THYROID DYSFUNCTION, HYPOTHYROIDISM, HYPERTHYROIDISM, RECRUITMENT | Participants were the offspring of the Safety and Efficacy of Lipiodol in Fertility Investigations (SELFI) study, conducted in Auckland. The study was approved by the Northern B Health and Disability Ethics Committee (Ministry of Health; 19/NTB/52) and registered with the Australian New Zealand Clinical Trials Registry (ANZCTR: 12620000738921). The aim of the SELFI study was to assess the magnitude and pattern of iodine excess and thyroid dysfunction following an OSCM HSG. The SELFI study cohort consisted of 196 consecutive consenting women who underwent OSCM HSG from July 2019 to April 2021, without overt hypothyroidism or hyperthyroidism, and who had not undergone recent hysterosalpingography. Participants had baseline assessments of urine iodine concentrations (UIC), TSH, FT4, and free triiodothyronine (FT3), and serial measurements of the same parameters for six months. More details on recruitment and assessments can be found in the published protocol (Flow diagram for the SELFI Study including the women who underwent oil-soluble contrast medium (OSCM) hysterosalpingography (HSG), those who became pregnant, and the subsequent number of newborns included in this study. Dashed black line indicates the newborns whose parents declined the Day 7 thyroid function test.All 57 babies had whole-blood TSH levels tested (from Guthrie cards) under New Zealand’s Newborn Metabolic Screening Programme 48 hours after birth. In addition, day 7 thyroid function tests were performed on newborns whose parents provided written informed consent for heel-prick sampling. The latter provided serum samples for thyroid function tests measuring concentrations of TSH, FT4, and FT3. No physical examinations were performed on these neonates, but data on sex, gestational age, birth anthropometry (weight, length, and head circumference), ethnicity, and mode of delivery were obtained from hospital records. Ethnicity of the newborn was reported by the parents, with a single ethnicity ascribed using the established hierarchical classification (First-morning urine samples were used in mothers to measure UIC by inductively coupled plasma mass spectrometry (ICP-MS) using Agilent 7700 [coefficient of variation (CV) of 10.8%]. The assay for the newborn screening TSH was the GSP™ Neonatal hTSH (CV-8%). Serum TSH, FT4, and FT3 concentrations were measured with an electrochemiluminescence immunoassay using an ADVIA Centaur XP analyzer (Siemens Healthcare Diagnostics Inc., Tarrytown, NY, USA), with CVs ≤5% ( | PMC9927197 |
Statistical analyses | Pearson’s correlation coefficients (Additional general linear models were run for all offspring outcomes, including both maternal predictors (i.e., peak UIC and TSH levels) and their interaction term. If the latter was statistically significant, the interaction between the two predictors and a given outcome was illustrated with a contour plot.Peak UIC levels were log-transformed to approximate a normal distribution. Statistical analyses were run using SAS v9.4 (SAS Institute, Cary, NC, USA). All statistical tests were two-tailed, with significance set at p<0.05. | PMC9927197 | ||
Results | NVD | THYROID | All 57 newborns were screened by the national programme, but 16 parents declined the thyroid function tests on their babies at day 7 (Demographic and clinical characteristics of the offspring born in the SELFI study who were screened under the New Zealand newborn metabolic screening programme and those who also had a thyroid function test (TFT) at Day 7.Data are the mean ± standard deviation or n (%).LSCS, lower segment caesarean section; NVD, normal vaginal delivery; TFT, thyroid function test.
Thyroid function tests were performed on time at day 7 in 76% of subjects and within nine days in 90%. The remaining four tests were performed between 11 and 20 days. The delay from the proposed date was caused by the government-imposed lockdowns during the COVID-19 pandemic (Results of thyroid function tests in the SELFI Study offspring.FT3, free triiodothyronine; FT4, free thyroxine (tetraiodothyronine); Q1, quartile 1 (25
There were no observed associations between maternal peak TSH or peak UIC and offspring parameters at birth or on Day 2 TSH (Linear associations between maternal urine iodine concentrations (UIC) and peak thyroid-stimulating hormone (TSH) levels during pregnancy and offspring outcomes.Data are the Pearson’s correlation coefficients (r) and respective p-values, with statistically significant associations at p<0.05 shown in bold.UIC values were log-transformed to approximate a normal distribution.FT3, free triiodothyronine; FT4, free thyroxine (tetraiodothyronine); and TSH, thyroid-stimulating hormone.Linear associations between maternal peak urine iodine concentrations (UIC) during pregnancy and Day 7 thyroid function parameters (serum) in the offspring: Maternal peak TSH levels during pregnancy were also associated with lower Day 7 FT3 levels in the offspring (When potential interactions between maternal peak TSH and peak UIC during pregnancy were examined, there was evidence of an interaction between those two parameters and their associations with Day 7 serum FT3 (p=0.018), but not TSH (p=0.87) or FT4 (p=0.62). For FT3, the lowest Day 7 levels were observed in the offspring of mothers with both high peak TSH and high peak UIC ( | PMC9927197 |
Discussion | hypothyroidism, asphyxia, congenital hypothyroidism, prematurity, primary hypothyroidism, infertility, primary thyroid dysfunction, sepsis | HYPOTHYROIDISM, ASPHYXIA, CONGENITAL HYPOTHYROIDISM, PRIMARY HYPOTHYROIDISM, COMPLICATION, SYNDROME, SEPSIS, SUBCLINICAL HYPOTHYROIDISM, PERMANENT CONGENITAL HYPOTHYROIDISM | This prospective offspring cohort study examined neonatal screening TSH data of 57 newborns whose mothers received pre-conceptional OSCM HSG, finding no cases of transient subclinical hypothyroidism or permanent congenital hypothyroidism. This is reassuring and is consistent with studies from China (The only study that had previously shown an increased risk of primary hypothyroidism in newborns conceived following OSCM HSG was from Japan, where 2.4% of screened neonates (5/212) had an abnormal newborn screening result, and 0.94% (2/212) had primary hypothyroidism, noting that the background rate of congenital hypothyroidism in Japan is only 0.07% (The association of maternal iodine levels with newborn’s Day 7 thyroid function was an unexpected finding, suggesting that iodine excess might have a direct, long-term effect on fetal thyroid function or fetal thyroid hormone regulation. The possibility of low T3 syndrome was considered less likely because there were no risk factors in our cohort, such as prematurity, asphyxia, sepsis, or other factors predisposing to a sick euthyroid status. Interestingly, similar findings in the offspring of rats receiving comparable doses of excess iodine during pregnancy have recently been shown with low TSH and low normal FT4 and FT3 (Overall, primary thyroid dysfunction in newborns is not a complication following maternal OSCM HSG. However, some newborns, especially those exposed to higher levels of iodine, appear to have subtle changes in the hypothalamic-pituitary-thyroid axis, and further studies are required to confirm and assess whether these changes are present at older ages. There is also a possibity for transient fetal hypothyroidism from iodine excess, which recover by birth and could not have be assessed in the current study. For the above reasons, neurocognitive assessments of these children would be required to ascertain whether there are any long-term developmental issues.The main limitation of our study was the lack of a control population to compare the incidence of similar subtle changes in thyroid function. While our study population was not particularly large (57 newborns), it is difficult to prospectively study a larger number of offspring because pregnancy occurs only in a relatively small proportion of women who undergo HSG. Nonetheless, the present work is the only prospective investigation confirming normal neonatal thyroid status by serum thyroid function testing in the offspring conceived after OSCM HSG. Another key strength of this study was the availability of maternal parameters, allowing us to examine potential associations with offspring thyroid function and not performed in any previous studies. As there is widespread and growing popularity for the use of OSCM HSG as an infertility investigation and treatment modality, identifying the potential impacts of the associated iodine excess on the offspring is of clinical importance to inform specialists and infertile couples about the potential impacts of this intervention. | PMC9927197 |
Conclusions | primary hypothyroidism | PRIMARY HYPOTHYROIDISM | This study confirms there is no increase in neonatal primary hypothyroidism in the offspring conceived following a standard OSCM HSG procedure. However, subtle central dysregulation of thyroid function may have occurred in some of these offspring. Future studies should be directed to assess the persistence of these changes in thyroid function and the potential neurodevelopment effects on these children. | PMC9927197 |
Data availability statement | The raw data supporting the conclusions of this article are not publicly available, but are available from the corresponding author on reasonable request and following the appropriate ethics approval. | PMC9927197 | ||
Ethics statement | The study was approved by the Northern B Health and Disability Ethics Committee, New Zealand (Ministry of Health; 19/NTB/52). Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin. | PMC9927197 | ||
Author contributions | RS, DM | PH, NJ, JP, RS, and SO’S conceptualized the study; DM conducted the study and drafted the initial manuscript; NH and DW assisted with newborn data collection, which were analysed by JD, DM, and PH. All authors contributed to the article and approved the submitted version. | PMC9927197 | |
Acknowledgments | We would like to acknowledge Guerbet for the research grant to the Liggins Institute. We also thank Janene McMillan (University of Auckland) for her invaluable help organizing the newborn investigations, and Alice Wang (University of Auckland) for managing the SELFI accounts. | PMC9927197 | ||
Conflict of interest | RS, JP | NJ is involved in research with the University of Auckland and the University of Adelaide, which are funded by Guerbet; NJ has undertaken paid consultancies for Guerbet; DM and PH are involved with a University of Auckland study on Lipiodol safety through an unrestricted independent grant to the Liggins institute from Guerbet; PH has received fees for speaking in two webinars sponsored by Guerbet; RGS and JMP have been paid for presenting and being advisory board members by Guerbet; RS, JP, and NJ undertake Lipiodol HSGs as a part of their profession.The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. | PMC9927197 | |
Publisher’s note | All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. | PMC9927197 | ||
Supplementary material | The Supplementary Material for this article can be found online at: Click here for additional data file. | PMC9927197 | ||
References | PMC9927197 | |||
2. Materials and Methods | This study deployed a between-subject longitudinal design to examine the effects of eccentric-oriented vs. traditional strength training protocols on crucial return-to-sport performance outcomes in professional team sport players during the late-stage ACL rehabilitation phase. | PMC10305302 | ||
2.1. Subjects | chondral defects, first-division | The sample for this study was selected out of 134 ACL patients who underwent rehabilitation in a rehab-specialized fitness facility (the Center of Excellence in Sport Science, Novi Sad, Serbia) between January 1, 2018, and December 31, 2022. In order to be selected for this study, subjects had to be professional team sports athletes, members of at least first-division teams in their respective sport. Subjects with previous ACL injuries or severe chondral defects were not included in the study, but meniscus repair or meniscectomy performed at the time of ACL reconstruction was tolerated. In addition, exclusion criteria were as follows: (1) fail more than 20% of all training sessions; (2) fail two consecutive sessions. The number of participants was estimated using G*Power 3.1. Power was set at 80% with an alpha level of 5%, and peak isometric force was considered a primary outcome, resulting in a sample size of 10 subjects per group. Finally, twenty-two subjects (soccer n = 8; basketball n = 9; handball n = 5; 14 males, 8 females, age range 16–30, age 19.9 ± 4.4 years, mass 77.4 ± 15.6 kg, height 182.4 ± 11.7 cm) (mean ± SD) with a unilateral reconstructed ACL (all performed with BTB grafts by the same surgeon) were included in the study. At the time of first testing, they were in the late-stage rehabilitation phase, between 5–6 (5.7 ± 0.4) months post-surgery. Participants were randomly allocated (lottery method) to the control group (CON: n = 11 (4 females), age 19.1 ± 2.1 years, mass 76.6 ± 16.5 kg, height 182.5 ± 10.2 cm) or experimental group (ECC: n = 11 (4 females), age 21.8 ± 4.6 years, mass 82.7 ± 16.6 kg, height 185.4 ± 12.2 cm). Throughout the study, the subjects were advised to maintain their nutritional habits and not take any nutritional supplements, especially protein or creatine supplements. Participants or their parent/legal guardian gave their written informed consent and were instructed to be free to ask questions on any occasion. In addition, they could withdraw from the study at any time without explanation. The ethics committee of the University of Novi Sad, Serbia, approved this study (protocol number: 122/2020). | PMC10305302 | |
2.2. Study Design | inflammation, swelling | INFLAMMATION | All participants were engaged in a standard rehabilitation program and supervised by two highly skilled practitioners (MSc and PhD in Sport Science with more than 10 years of experience and 200 ACL rehabilitations) until enrolled in the study (During the early stage of rehab (around 12 weeks), care was taken to decrease swelling and inflammation and restore range of motion and muscle activity in the affected muscles. Running was allowed after the respective physician’s clearance, somewhere around 12 weeks post-op. Low-load strength training (open and closed chain), landing skills, and low-load plyometrics were progressively included at around 16 weeks post-surgery (mid-stage rehabilitation). All participants enrolled in five training sessions a week, 80–90 min per session. Finally, between 5 and 6 months post-op, physician clearance for first RTS testing (initial testing) with recommended test protocols (3) was received for all patients and organized at the earliest occasion. After initial testing, each participant was randomized to the experimental or control group and started with the corresponding rehabilitation program. Final testing, identical to the initial one, was conducted five to seven days after the intervention period. All tests were performed by an experienced strength and conditioning coach who was blinded to the present study protocol design. In addition, tests were performed at the same time of day (16:00 p.m.–18:00 p.m.) and under the same environmental conditions for all subjects (22 °C and 60% humidity). All participants were strongly instructed to abstain from any strenuous activity for at least 24 h before testing. | PMC10305302 |
2.3. Rehabilitation Protocols | CONTRACTIONS | The training period was 6 weeks long, with training occurring 6 days per week for participants in both groups. As a core of the rehabilitation protocol, two to three strength training sessions were conducted per week (15 training sessions in total), with eccentric-oriented vs. traditional strength training for the ECC and CON groups, respectively (Beyond this, participants enrolled in two/three training sessions per week consisting of medium to high aerobic load (treadmill running), upper body strength, low to moderate load agility, and deceleration/landing skills. Finally, one/two training sessions per week were dedicated to recovery procedures (These training sessions were identical for both groups considering drill selection, sets, reps, and rest periods, but not intensity considering the greater torque produced during eccentric contractions. All training sessions were performed in a one-on-one format by the same highly experienced strength and conditioning coach, who guided participants on how to perform each exercise. The subjects initiated each training session with a standardized warm-up on a stationary bicycle (5–7 min), followed by dynamic stretching, calisthenics, and preparatory exercises (15 min in total). The exercise load was periodized and increased progressively throughout the study period, with deloading in the third and sixth weeks. | PMC10305302 | |
2.4. Testing Procedures | PMC10305302 | |||
2.4.1. Isometric Leg Strength | The isometric leg strength test was executed on a flywheel device (D11 full, Desmotec, Biella, Italy), with peak force measured as an outcome. The participant wore a waist-fastened harness anchored to the strap and attached to the device, tightened so as not to allow vertical movement of the participant. The device has two load cells connected to a software-equipped computer (D.Soft, Desmotec, Biella, Italy). From a semi-squat position (100 degrees knee angle, hands on hips) and following a signal, the participant tries to stand upright, progressively developing maximal pressure on the plates for a total of 10 s. The measured force is read and saved on the computer. The better of the two obtained results (rest period of 2 min between trials) for both injured (ISOSI) and uninjured (ISOSU) legs, expressed in kilograms, was recorded and used in further analysis. Intra-class correlation coefficients showed excellent reliability for both ISOSI (ICC: 0.96; CI: 0.90–0.98) and ISOSU (ICC: 0.96; CI: 0.91–0.98). | PMC10305302 | ||
2.4.2. Hop Tests | For the single-leg hop test (SLHU and SLHI for uninjured and injured legs, respectively), the participant is positioned on one leg, jumps horizontally with an all-out effort, and lands on the same limb with a controlled, balanced landing. With the triple jump test (TLHU and TLHI for uninjured and injured legs, respectively), the patient is positioned on one leg, performs three consecutive horizontal jumps with an all-out effort, and lands on the same limb in a controlled manner. The hop distance for all hop tests was measured to the nearest centimeter from the starting line to the patient’s heel with a standard tape measure. Two successful trials for each limb were recorded for all tests, with the highest distances used to compute a limb symmetry index ([injured side/uninjured side;] × 100%). A limb symmetry index of <100 reveals a deficit in the injured limb. Intra-class correlation coefficients showed excellent reliability for both SLHU (ICC: 0.92; CI: 0.82–0.97) and SLHI (ICC: 0.83; CI: 0.60–0.93), with a poor limb symmetry index for the single-leg hop test (limb symmetry index, SLHLSI) (ICC: 0.50; CI: 0.19–0.79). In addition, intra-class correlation coefficients showed excellent reliability for both TLHU (ICC: 0.92; CI: 0.82–0.97) and TLHI (ICC: 0.83; CI: 0.60–0.93), with an acceptable limb symmetry index for the triple-leg hop test (limb symmetry index, TLH-LSI) (ICC: 0.78; CI: 0.48–0.90). | PMC10305302 | ||
2.4.3. Vertical Jump Tests | To perform a countermovement jump (CMJ), subjects were instructed to start with hands on the hips in an upright standing position, swiftly flex their knees to a semi-squat position, and immediately jump upward as high as possible while landing with knees extended. A contact mat (Just Jump, Probotics, Huntsville, AL, USA) measures the flight time, from which the flight height in centimeters is calculated. Intra-class correlation coefficients showed excellent reliability for CMJ (ICC: 0.98; CI: 0.96–0.99).For single countermovement jumps (SLJU and SLJI for uninjured and injured legs, respectively), athletes started from an upright single-leg standing position on the contact mat with hands on the hips. After dynamically counter-moving to a self-selected depth, they jumped vertically with maximum effort and landed on the same leg. The best out of three trials for both vertical jump tests were recorded (Just Jump, Probotics, Huntsville, AL, USA) and used for further analysis. Limb symmetry index (SLLSI) was calculated. Intra-class correlation coefficients showed excellent reliability for both SLJU (ICC: 0.92; CI: 0.81–0.96) and SLJI (ICC: 0.94; CI: 0.87–0.97), with an acceptable limb symmetry index for the single-leg jump test (limb symmetry index, SLJ-LSI) (ICC: 0.71; CI: 0.31–0.88). | PMC10305302 | ||
2.5. Statistical Analysis | Test results are presented as the mean ± standard deviation (SD). Before any statistical analysis, the normal distribution and homogeneity of the data were confirmed with the Shapiro-Wilk and Levenes tests, respectively. The test-retest reliability was assessed using an intraclass correlation coefficient (ICC) two-way mixed model and interpreted as follows: ≥0.9 = excellent; ≥0.8 = good; ≥0.7 = acceptable; ≥0.6 = questionable; ≥0.5 = poor; <0.5 = unacceptable [A two-way ANOVA (2 × 2, group × time) was used to analyze the effects of eccentric training on the study outcomes. The percentage of change ([post value/pre value] − 1) was computed and reported for each variable. When the sphericity assumption was violated, the Greenhouse-Geisser correction was used for interpretation. Moreover, effect sizes (ES) were determined from ANOVA output by converting partial eta squared to Cohens d, with ES values considered to be either “trivial” (<0.20), ”small” (>0.2–0.6), “moderate” (>0.6–1.2), “large” (>1.2–2), or “very large” (>2). The level of significance was set at | PMC10305302 | ||
3. Results | test-related injuries | All participants attended all training sessions without reporting any rehabilitation or test-related injuries. ECC and CON groups were similar for age (21.8 ± 4.6 vs. 19.1 ± 2.1 years), body mass (82.7 ± 16.6 vs. 76.6 ± 16.5 kg), and height (185.4 ± 12.2 vs. 182.5 ± 10.2 cm).A two-way analysis of variance revealed significant main effects of time across training for all dependent variables (posttest > pretest, A significant interaction effect was found for ISOSU and ISOSI, with very large and large effect sizes, respectively. Comparing the results of the pretest and posttest measurements, the ECC group had an improvement of 28.1% vs. 15.1% for the CON group for ISOSU, while for ISOSI, improvements were 27.1% and 18.1% for the ECC and CON groups, respectively. A significant interaction effect was found for CMJ, with a very large effect size. The experimental group and control group achieved progress of 12.9% and 6.7%, respectively. A significant interaction effect was observed for the single-leg jump-injured leg variable (SLJI) with a large effect size. Considering the percentage of improvements, 23.8% and 13.7% were reported for the ECC and CON groups, respectively. For the single-leg hop test-injured leg, the interaction effect showed statistically significant differences between groups, with a large effect size. When expressed as a percentage, the reported improvements were 23.9% and 8.1% for the ECC and CON groups, respectively. Finally, significant group-by-time interactions were found for the triple hop test-injured leg (TLHI), with a large effect size. The experimental group and control group achieved progress of 14.3% and 5.3%, respectively. | PMC10305302 | |
4. Discussion | BLIND | The present investigation aimed to compare the effects of 6 weeks of eccentric-oriented vs. traditional strength training on return-to-sport outcomes in late-stage ACL rehabilitation in professional team sport athletes. The study results showed that, although both training programs significantly improved all tested parameters, the eccentric-oriented training program resulted in significantly greater improvements concerning lower body strength, vertical jump performance, single-leg jump with injured leg (SLJI), single-leg hop with injured leg (SLHI), and triple-leg hop with injured leg (TLHI) performance. No significant training-effect differences were determined for isometric strength (limb symmetry index), single-leg jump test (uninjured leg), single-leg jump test (limb symmetry index), single-leg hop test (uninjured leg), single-leg hop test (limb symmetry index), triple-leg hop test (uninjured leg), and triple-leg hop test (limb symmetry index). Thus, eccentric-oriented training can be considered a worthy functional performance-improvement training method in professional team sports for late-stage ACL surgery patients. It is generally recognized that strength training is a cornerstone of every sound ACL rehabilitation program [In a 6-week study by Kasmi et al. [Several other articles addressed the effects of eccentric training on distinct return-to-sport performance outcomes in the non-professional athlete population. Lepley et al. [No significant training effect between eccentric and traditional training for limb symmetry indexes was found, with several possible explanations for the obtained results. As presented in Our study results showed greater efficacy of eccentric-oriented than traditional rehabilitation programs in injured leg performance, while no differences between training programs for uninjured leg performance were shown. Mechanisms that are likely responsible for the obtained results should be concisely hypothesized. First, continuing use of eccentric exercise is able to improve muscle morphology, with increases in fascicle length and cross-sectional area while targeting type II fibers being regularly established [A few study limitations are noteworthy. First, assessment of the hamstring strength/power parameters should be included in the return to sport test battery to allow more intergroup comparison. Second, due to the nature of the intervention, it was not possible to blind the strength and conditioning coach to group allocation. Third, the small sample size in each group limits the statistical power and precludes the generalizability of study findings. Notwithstanding these limitations, the effectiveness of eccentric-oriented training on leg strength and power-related performance outcomes in late-stage ACL-rehabilitation professional team sport players is supported by the present study findings. | PMC10305302 | |
Author Contributions | Conceptualization, M.D.M.S. and R.M.M.; Methodology, N.A., M.T. and B.V.; validation, M.T., M.M. and D.-N.Z.-Ș.; formal analysis, R.M.M. and M.T.; investigation, M.M.; resources, R.M.M. and M.D.M.S.; data curation, N.A., M.M., N.V. and D.-N.Z.-Ș.; writing—original draft preparation, M.D.M.S. and R.M.M.; writing—review and editing, B.V. and N.A.; project administration, N.V. All authors have read and agreed to the published version of the manuscript. | PMC10305302 | ||
Institutional Review Board Statement | The study was conducted according to the guidelines of the declaration of Helsinki and approved by the ethical committee of the University of Novi Sad (protocol number: 122/2020, approved date: 16 October 2020). | PMC10305302 | ||
Informed Consent Statement | Participants provided informed consent for their participation in the study. | PMC10305302 | ||
Data Availability Statement | The data are available upon reasonable request. | PMC10305302 | ||
Conflicts of Interest | The authors declare no conflict of interest. | PMC10305302 | ||
References | ± | Experimental protocol for the present study.Strength training program for the EXP and CON groups.Rehabilitation program weekly structure.Within-group differences, main and interaction effects, and effect sizes for selected variables.Pretest—initial test result ± standard deviation; Posttest—final test result ± standard deviation; * Significant difference ( | PMC10305302 | |
Background | nonadherence, cardiometabolic diseases, DDIs, chronic disease | DISEASE PROGRESSION, CHRONIC DISEASE, DRUG-DRUG INTERACTION | Disentangling nonadherence (NA), drug-drug interactions (DDIs), and disease progression from each other is an important clinical challenge for providers caring for patients with cardiometabolic diseases. NAs and DDIs are both ubiquitous and often overlooked. We studied a novel chronic disease management (CDM) test to detect medication adherence and the presence and severity of DDIs. | PMC10105436 |
Materials and methods | We conducted a prospective, randomized controlled trial of 236 primary care physicians using computer-based, simulated patients, measuring clinical care with and without access to the CDM test. The primary outcomes were whether use of the CDM test increased the accuracy of diagnoses and ordering better treatments and how effective the intervention materials were in getting participants to order the CDM test. | PMC10105436 | ||
Results | DISEASE PROGRESSION | Physicians given the CDM test results showed a + 13.2% improvement in their diagnosis and treatment quality-of-care scores (p < 0.001) in the NA patient cases and a + 13.6% improvement in the DDI cases (p < 0.001). The difference-in-difference calculations between the intervention and control groups were + 10.4% for NA and + 10.8% for DDI (p < 0.01 for both). After controlling for physician and practice co-factors, intervention, compared to control, was 50.4x more likely to recognize medication NA and 3.3x more likely to correctly treat it. Intervention was 26.9x more likely to identify the DDI and 15.7x more likely to stop/switch the interacting medication compared to control. We found no significant improvements for the disease progression patient cases. | PMC10105436 | |
Conclusion | nonadherence | DISEASE PROGRESSION | Distinguishing between nonadherence, drug-drug interactions, and disease progression is greatly improved using a reliable test, like the CDM test; improved diagnostic accuracy and treatment has the potential to improve patient quality of life, medication safety, clinical outcomes, and efficiency of health delivery. | PMC10105436 |
Trial Registration | clinicaltrials.gov (NCT05192590). | PMC10105436 | ||
Supplementary Information | The online version contains supplementary material available at 10.1186/s12875-023-02042-4. | PMC10105436 | ||
Keywords | PMC10105436 | |||
Background | cardiometabolic diseases, DDIs | DISEASE PROGRESSION, CHRONIC DISEASE | Treating cardiometabolic diseases relies on sustained efforts ranging from lifestyle modification, public health measures, and procedural interventions to achieve better health outcomes [The physician, already challenged to prescribe the appropriate medication at the correct dose, must also hope that patients take their prescribed medication. Numerous studies indicate > 40% of patients with chronic conditions do not correctly follow their prescribed drug regimens, which is due to many factors including the sheer volume of medications they are prescribed [Adding to the challenge, physicians and other providers must not only disentangle nonadherence from DDIs but also separate these two issues from disease progression [An important reason physicians have difficulty in distinguishing nonadherence from DDIs and disease progression is lack of an effective and standardized method to test for these conditions [Prior to this study, scientists at Aegis Sciences Corporation have developed a Chronic Disease Management (CDM) test that detects medication adherence and the presence and severity of DDIs. The CDM test detects ingested substances from an oral fluid sample and tests for 150 of the most common cardiometabolic medications, including angiotensin-converting enzyme inhibitors, angiotensin receptor blockers, beta blockers, diuretics, statins, sulfonylureas, antithrombotic agents, and vasodilators. The CDM test provides the physician with qualitative adherence data on these medications and also identifies potential DDIs. Through Aegis, we were allowed use of simulated CDM test results and funded to determine whether clinical practice change occurred.We used simulated patients in a randomized controlled intervention trial (RCT) to evaluate whether simulated results from the CDM test changed primary care physician behavior and led to better, more accurate diagnoses and treatment among patients presenting with chronic cardiometabolic diseases and improved their ability to distinguish between medication nonadherence, DDIs, or disease progression. | PMC10105436 |
Methods | PMC10105436 | |||
Overview | cardiometabolic disease, ®, DDIs | DISEASE PROGRESSION | Between October 2021 and February 2022, we conducted a prospective, cross-sectional RCT using simulated Clinical Performance and Value® (CPV®) vignettes. The study was conducted online and assessed the clinical utility of the CDM test among United States primary care physicians (PCPs) in the evaluation, work-up, diagnosis, and management of patients with chronic cardiometabolic disease with either NA, DDIs, or disease progression. We measured the participating physicians’ clinical care before and after introduction of the CDM test. Participants each cared for three simulated CPV® patients per two rounds, for a total of six patient cases. | PMC10105436 |
Ethics | This study was conducted in accordance with ethical standards, approved by the Advarra Institutional Review Board, Columbia, MD, USA, and listed in clinicaltrials.gov (NCT05192590, 14/01/2022). Voluntary, informed consent was obtained from all participants. | PMC10105436 | ||
Sample Size Calculation | From previous work, we know that a 5% change in CPV scores are both statistically and clinically significant. [ | PMC10105436 | ||
Physician Selection | HF, HTN, atrial fibrillation, heart failure, hypertension | RECRUITMENT, HTN, ATRIAL FIBRILLATION, HEART FAILURE, HYPERTENSION, CORONARY ARTERY DISEASE, DIABETES (DM) | We recruited and enrolled practicing PCPs from a national roster. The eligibility criteria were: (1) board-certified, (2) currently practicing in internal or family medicine between 2 and 35 years, (3) practicing in a community or non-academic setting, (4) caring for more than 40 patients weekly, (5) commonly treating patients with cardiometabolic conditions such as atrial fibrillation (Afib), coronary artery disease, heart failure (HF), diabetes (DM), and hypertension (HTN), (6) practicing in the United States, (7) English-speaking, and (8) able to access the internet. All eligible participants were randomly assigned using a coin flip methodology into one of three study arms. Recruitment continued until we reached at least the sample size as calculated above.261 PCPs met the inclusion criteria. Of this total, 8 participants withdrew from the study and 17 participants failed to complete all six patient cases. 236 physicians completed 1,416 simulated patient cases. Of these 236, 76 were in control, 77 in intervention 1, and 83 in intervention 2. In the first round of patient cases, all physicians were naïve and without access to the CDM test results. | PMC10105436 |
Intervention | We provided identical educational materials consisting of a physician-targeted slide deck, a fact sheet detailing the science behind the test, the sample collection method, and a sample report to both intervention groups, who were required to view these materials before progressing to round 2 and completing the study. Two weeks after reviewing the educational materials, both intervention groups were asked to complete three new CPVs during round 2 of the study.Intervention 1 were given the CDM test results as they completed the round 2 patient cases, and intervention 2 had the option of ordering the CDM test while caring for their round 2 patients. The simulated CDMT results could be viewed immediately after ordering and are available in a format that is identical to the actual CDMT result. Control arm physicians in round 2 had access to the current standard of care diagnostic tools but did not have access to the CDM test results. | PMC10105436 | ||
Data sources | We had two sources of data: the physician survey and the physicians’ responses to the CPVs. | PMC10105436 | ||
Physician survey | After physicians were enrolled into the study, they were asked to answer a brief questionnaire detailing their practice characteristics, their patient level and types, and their own demographic background. The survey included questions on employment status, location of practice, practice type, and patient make-up, among others. | PMC10105436 | ||
Clinical Performance and Value (CPV) vignettes | CPVs are a validated online patient simulation tool owned by QURE Healthcare which have been widely used to measure clinical care [With between 61 and 74 evidence-based criteria evaluated for each CPV, participant responses were scored by two trained expert physicians, working independently, using pre-determined criteria (see Supplement 1) based on current standards of care to measure individual physician care. In cases of disagreement, a third physician would adjudicate for the final score. All three physicians were blinded to the study arm assignment of the participant. We generated a quality-of-care percentage score based upon the number of responses matching the evidence-based criteria (range from 0 to 100%). Higher percentage scores indicated greater adherence to the evidence base in clinical care provided.We note the DxTx score has proven helpful to understand the challenges clinicians face—DDI DxTx scores were 22.9% in our previous research indicating that both diagnosis and treatment of DDIs are significant clinical problems [ | PMC10105436 | ||
Chronic cardiometabolic disease vignettes | nonadherence, DM/HTN | DISEASE PROGRESSION, DISEASE | We constructed nine CPV vignettes on a 3 × 3 matrix with three patient case types and three variants (see Supplement 2). The case types included patients with Afib, HF, or DM/HTN. The three variants included patients who were not at their therapeutic goal because of: medication nonadherence but no DDI (NA); because of a DDI but adherent to their medications (DDI); and disease progression who were both adherent to their medications and had no DDI (AND). For the AND patients, a diagnosis of disease progression was made with either explicitly diagnosing worsening disease, selecting a new medication, increasing the dose of medication, or referring to a specialist. To avoid ordering effects, every participant cared for three CPV patients, one randomly assigned from each case type and from each variant type. At the end of the two rounds, each participant completed a total of six patient cases, three patients per round, one from each case type and case variant, with no provider seeing the same patient more than once. | PMC10105436 |
Study outcomes and analysis | REGRESSION | The study sought to determine the clinical utility of the CDM test. Accordingly, the primary outcome is whether using CDM test improved patient care by increasing the diagnosis of NA or DDI Secondary outcomes included the effect of provider and clinical practice characteristics on care, cost implications of using the CDM test, and identifying the best use cases of the CDM test. The effect of provider and clinical practice characteristics were determined by inserting these variables into the multiple variable regression models. Cost analysis was done by measuring differential rates of diagnostic ordering selected by each arm and multiplying by average Medicare reimbursement rates for these workups. Use case determination was made through difference-in-difference and logistic subanalyses of the clinical variants presented to the participants.All analyses were done in Stata 14.2. | PMC10105436 | |
Patient and Public Involvement | No patients involved. | PMC10105436 | ||
Results | PMC10105436 | |||
Physician Characteristics | 236 board-certified PCPs met the eligibility requirements, completed the physician questionnaire and six CPV patient cases (Table
Physician Baseline Characteristics, by Study Arm*One control participant missing data | PMC10105436 | ||
Diagnosis-and-Treatment Domain Scores | cardiometabolic diseases, DM/HTN | WEST | At baseline, we found wide variation in DxTx scores among participants caring for patients with cardiometabolic diseases. Across all patient cases, DxTx ranged from 0 to 75%, averaging 21.7%
Diagnosis-Treatment Scores by CPV VariantWe then compared control to the first intervention in a pre-post analysis. The formal difference-in-difference estimations using a fixed effects model showed a + 10.4% improvement in recognizing and treating NA and a + 10.8% improvement for identifying and treating DDI (p < 0.01 for both). There was no round over round improvement in the DxTx score for the AND patient cases.After controlling for gender, internal medicine specialty, age, region, practice locale and type, the fixed-effects model showed practicing in the West (+ 4.3%, 95% C.I. +2.2% to + 6.4%) and in non-urban environments (+ 2.1%, 95% C.I. +0.3% to + 3.9%) were correlated with higher DxTx scores. Comparing intervention to control, we found intervention 1 providers performed significantly better than controls across all patient cases (+ 4.5%, 95% C.I. +0.6% to + 8.3%). By case variant, the intervention group improved significantly for both the NA (+ 10.8%, 95% C.I. +3.4% to + 18.2%) and DDI cases (+ 11.0%, C.I. +4.2% to + 17.9%), but not for the AND cases (-7.6%, 95% C.I. -12.6% to -2.6%).By case type (Afib, HF, and DM/HTN), we found no significant improvement in DxTx score for the intervention group in the difference-in-difference fixed effects modeling (Afib: +2.9%; HF: +3.8%; DM/HTN: +6.7%; p > 0.05 for all). However, we did see improved identification of both NA and DDI across all case types (Afib, O.R. 39.7, 95% C.I. 5.1-309.4; HF, O.R. 19.2, 95% C.I. 1.6-230.7; DM/HTN, O.R. 99.0, 95% C.I. 8.8-1179.5). | PMC10105436 |
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