How Does Physical Health Influence the Perception of Optimism? A Longitudinal Social Network Analysis

How Does Physical Health Influence the Perception of Optimism? 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A Longitudinal Social Network Analysis Claudia Tejada-Gallardo, Ana Blasco-Belled, Carles Alsinet This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6310226/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background: Extensive literature has explored the association between optimism and enhanced physical health. Despite this well-established link, understanding specific mechanisms by which physical health can influence the perception of optimistic peers remains a challenge. The biosocial perspective highlights the role of biological differences in the development of positive psychological attributes, such as optimism, while also recognizing the significant influence of the social environment on this relationship. In this study, we aim to explore the extent to which physical health symptoms predict the perception of being perceived as an optimistic individual. Method: A cohort of 240 university students across 14 undergraduate classes participated in this longitudinal study, completing two assessments spaced seven months apart. Participants completed (1) self-report measures on physical health symptoms, encompassing sleep disturbances, headaches, respiratory infections, and gastrointestinal problems, and (2) pencil-and-paper tasks to identify classmates whom they perceived as optimistic individuals. Employing a longitudinal social network analysis approach, we utilized the temporal exponential-family random graph model (TERGM) in Rstudio to analyze the influence of self-reported physical health symptoms on the selection of optimistic peers. Results: Our findings revealed that symptoms associated with sleep disturbances and respiratory infections predicted less nominations of being perceived as an optimistic individual. Notably, the negative association with respiratory problems persisted over time. Conclusions: The presence of specific physical health issues can significantly impact how individuals are perceived by others, concerning optimism. These results expand existing literature by emphasizing the role of social judgments within peer networks. Optimism physical health social context social network analysis Figures Figure 1 Introduction Personality research has been interested in understanding how individual differences influence the broader social environment (Fowler et al., 2009 ). This line of inquiry has led to the examination of personality traits in interaction with social structures, encompassing the ways individuals navigate and integrate within social groups. In recent years, optimism has emerged as a key psychological trait of interest in this context. Defined as a general positive expectation about the future, optimism has been linked to numerous beneficial outcomes, including physical health (Scheier & Carver, 1985 ; Scheier & Carver, 2018 ). Extensive research indicates that individuals with higher levels of optimism are more likely to experience better overall health and recover more quickly from illness and medical procedures (Segerstrom et al., 2017 ). However, the inverse relationship—how physical health influences the perception of optimism, particularly within a social context—remains unexplored. Understanding this dynamic is needed for exploring the role of physical health in shaping individuals’ perceptions of optimism within a social context during the development of social ties. Optimism plays a role in the formation of social relationships, with several empirical evidence providing account for its contribution to social network ties and diversity. For instance, individuals who maintain an optimistic outlook towards the future possess larger, stronger, and more diverse social networks (Poulin & Boivin, 2000 ; Lyubomirsky et al., 2005 ). Maintaining optimistic expectations about the future may motivate individuals to seek out new connections and engage in social activities, increasing the likelihood of forming meaningful ties with other people (Heinrich & Gullone, 2006 ). Furthermore, optimism might contribute to social resilience, as optimistic individuals are more likely to nurture their social ties and navigate challenging situations during adverse circumstances (Fredrickson, 2001 ). A suitable framework for exploring the influence of optimism on social structures is to adopt a biosocial perspective that encompasses both biological and structural (i.e., social) mechanisms (Andersson, 2012 ). Within this framework, structural mechanisms involve the social structures in which individuals navigate, such as classrooms or neighborhoods, while biological mechanisms encompass genetic predispositions and physiological processes, including the manifestation of physical health symptoms (Freese, 2008 ). In social relationships, besides the transmission of emotions and attitudes, an individual’s physical health can also be noticed (Christakis & Fowler, 2009 ). Despite the biosocial perspective emphasizes the relevant role of the social context in the interaction between biological and psychological outcomes (Freese, 2008 ), it is a question that remains underexplored. Hence, recognizing the interplay between biological factors and social structures offers a more nuanced and realistic understanding of how optimism and social networks interact (Andersson, 2012 ). The Role of Physical Health Symptoms in Being Perceived as Optimistic Physical health symptoms refer to physical sensations or experiences, which can range from mild to severe, that may be indicative of an underlying health condition or illness (Katon, 2011 ). Physical health symptoms account for over half of all outpatient visits, from which approximately half of these include pain complaints (e.g., headaches), respiratory problems (e.g., cough, sore or throat), and non-specific symptoms (e.g., insomnia; Kroenke, 2003 ). The presence of physical health symptoms has an impact on individuals' overall well-being, affecting their emotional, social, and general physical health (Rasmussen et al., 2009 ). Individuals who experience these symptoms continuously exhibit a variety of psychopathological manifestations, including depression, anxiety, and impaired quality of life (Escobar et al., 2010 ; Hinz et al., 2017 ). The effects of physical health symptoms can also interfere with the interpersonal sphere (Kirmayer et al., 2004 ) by influencing the way individuals interact and form connections with others. In this process, optimism might play an important role given that negative attributions about one’s health status, such as poor health habits or chronic illness, are related to unfavorable perceptions of optimism (Conversano et al., 2010 ). When examining the influence of social interactions in the perception of optimism, methodological nuances need to be considered. For example, relying on self-reported data entails some limitations, such as missing the opportunity of incorporating relevant information from contextual sources that are contemplated within the biosocial model (e.g., peer-based reports), and providing scores influenced by social desirability and recall biases (Knoke & Yang, 2008 ). To overcome these limitations, both self- and peer-based reports should be included. For example, one could examine the influence of self-reported physical health symptoms in being perceived as an optimistic person, which is the aim of the current study. To do that, the social network analysis offers a suitable methodological framework to investigate questions including self- and peer-reported data (Fowler & Christakis, 2009 ). The Social Network Analysis The social network analysis focuses on studying the patterns of interpersonal relationships among individuals forming a network of ties (Scott, 1988 ). This approach offers a comprehensive perspective by integrating both self-reports and peer-nominations, providing several methodological advantages. By considering both self-rated physical health symptoms and peer-rated evaluations of optimism, we can overcome limitations related to incomplete self-knowledge and biased reporting (Back & Vazire, 2015 ; Vazire, 2010 ). This approach allows to investigate whether self-reported physical health symptoms can predict peer-rated nominations without the unwanted influence of method variance (Funder, 1995 ). As social network approach presents a valuable opportunity to investigate longitudinal changes in social networks (e.g., do other people see me as an optimistic person?) and explore the influence of biological factors on these dynamics (e.g., do these attributions depend on my physical health symptoms?) (Nestler et al., 2015 ; Wölfer et al., 2015 ), we employed this methodological approach to examine how physical health symptoms influence optimism from a peer-based perspective. A detailed explanation of this approach is presented in the Methodology section. Current Study The goal of the present study is to investigate the interplay between self-reported physical health symptoms and peer-based perceptions of optimism. More concretely, we will examine the extent to which self-reported physical health symptoms influence peer-reported perceptions of optimism (i.e., how does my physical health symptoms influence whether my peers see me as an optimistic person?) and its temporal evolution (i.e., do these attributions change over time?) in a given social structure (i.e., classrooms). The study included two measurement assessments, with the first measurement taking place the beginning of the first academic year, and the second taking place seven months later. Based on the premise that physical health issues can often lead to a less optimistic outlook (e.g., Rasmussen et al., 2009 ), we expect that self-reported physical health symptoms would negatively predict being perceived as an optimistic person by peers in the initial assessment (H1), and that this negative association would be maintained at the second assessment (H2). Methods Participants The present study comprised 15 groups of first-year university students aged between 18 and 32 ( M = 19.31, SD = 1.78, 75.8% females) from the University of Lleida (Spain). Undergraduates are a suitable target for this research because they need to adapt to a new social environment, where social (e.g., making new acquaintances) and academic challenges (e.g., adapting to new academic standards) can precede physical health symptoms (Beiter et al., 2015 ; Pascoe et al., 2020 ). One of the initial groups did not complete the second measurement assessment, therefore the final number of networks included were 14 each time, resulting in a total of 28 networks. Some participants were removed from the study because either responded wrongly to attention-check items ( n = 100) or did not complete the two measurement assessments ( n = 35). As a result, the final retained sample for the study included 240 individuals. The retention rate over the seven months was 58.5%. The participants were informed of the study goals and procedure, enrolled voluntarily in the study, and signed an informed consent allowing their data to be used anonymously for research purposes. Procedure Participants completed the two measurement assessments in classes. However, two out of the 28 groups completed it online at Time 1 and Time 2 due to COVID-19 restrictions. As first-year students, participants were mostly new to each other at Time 1. In contrast, until the measurement was repeated at Time 2, they had the chance to meet in class and interact during the rest of the semester. Even though restrictions due to COVID-19 were still present, the majority of groups assisted to face-to-face classes. Participants had the opportunity to freely interact with each other throughout the course and no instructions nor restrictions were provided about the way they could interact, leaving the formation of ties and group dynamics evolve within a naturalistic setting. To collect the data, we provided the participants with a Google Forms link containing the self-reports of physical health symptoms and a pencil-and-paper sheet including the full name of all the members of the class for the nomination task. After completing the self-reports, the participants had to mark those peers who in their opinion were optimistic. Participants were allowed to select as many peers as they wished, except themselves. This study was approved by the committee of the faculty of education, psychology and social work of the University of Lleida (Spain). Instruments Physical health symptoms were assessed with the Physical Health Questionnaire (PHQ; Schat et al., 2005 ) translated and back translated into Spanish. The scale included 14 items measuring the frequency with which respondents experienced four physical health problems: sleep disturbances, headaches, respiratory infections, and gastrointestinal problems. Items 1–11 were rated on a 7-point Likert scale ranging from 1 ( not at all ) to 7 ( all of the time ). Items 12 and 13 were rated from 0 ( zero times ) to 7 ( seven or more times ). An item example is “How many times have you had minor colds?”. Item 14 “When you had a bad cold or flu, how long did it typically last?”, was rated from 1 ( one day ) to 7 ( seven or more days ). Internal reliability coefficients (Cronbach’s alpha) at Time 1 were .82 for sleep disturbances, .85 for headaches, .79 for gastrointestinal problems, and .63 for respiratory infections. At Time 2 the internal reliability coefficients were .78 for sleep disturbances, .85 for headaches, .80 for gastrointestinal problems, and .60 for respiratory infections. Social networks were assessed with a pencil-and-paper task for peer nominations. At each measurement assessment, students were presented with a list of all peers’ names and were asked to select the classmates they considered to be optimistic. The selected individuals were coded as 1 (being nominated) and 0 (being non-nominated). Statistical Analysis The present study employed a multilevel design consisting of multiple time assessments and multiple groups. The network-analytic approach requires no missing values to enter networks as independent and progressive temporal steps to capture the time dependency effect, that is, whether there were differences in how a psychological variable (i.e., optimism) influenced peer nominations as time progressed. Therefore, only data from the participants who completed the two measurement assessments were included in the analyses. This approach has proven effective in previous research (Tejada-Gallardo et al., 2022). To analyze the dynamics of social networks, we used the social network analysis in RStudio (RStudio Team, 2020). We applied the exponential-family random graph model (ERGM) and its extension for longitudinal data, the temporal exponential-family random graph model (TERGM). Both ERGM and TERGM represent a general class of models that allow simulating a pattern of dependencies between a set of covariates and participants’ relationships within a social network. The ERGM lies on the assumption that independency cannot be met because the scores of participants among a delimited group are not independent of those of the rest of the participants. Hence, multiple dependent values were collected from each participant, which required the use of analytic methods suitable for dependent network data (within a group). TERGM was also suitable for this procedure because it allowed to include independent networks (i.e., 14 independent classes) and, at the same time, time dependency (i.e., two time points). The models employed in this study are used to study the relationships within a group (i.e., network). These relationships can depend on internal (endogenous) or external (exogenous) covariates. Exogenous covariates do not directly rely on the internal mechanism that create the network. In our study, the exogenous covariate was optimism, and it was used as an individual characteristic of the participants treated from an ego (sender: influence in nominating) and alter (receiver: influence in being nominated) perspective. It is important to evaluate certain universal dependencies according to the general network theory (Scott, 2000 ; Wasserman & Faust, 1994 ), which are included in the analyzed model (Wasserman & Faust, 1994 ). The common tendency to reciprocate nominations within a network was measured using a reciprocity parameter (mutual term), which controls for the overestimated selection effects of reciprocating relationships. The tendency to create triads was measured using a geometrically weighted edgewise shared partner (GWESP) distribution term. Generally speaking, in a triadic relationship, the transitivity effect is used to control for the overestimated selection effects (e.g., those individuals who become friends with their friends’ friends; Steglich et al., 2010 ). We also introduced the geometrically weighted out-degree (GWODEG) distribution term to control for the tendency to select almost everyone. Finally, we included an absolute difference term (absdiff) among the optimism scores for every interaction in each group. If significant, this estimate suggests that the absolute differences of optimism might influence the likelihood of a relationship between two participants. Results Main Effects: The Influence of Physical Health Symptoms on Perceived Optimism Descriptive statistics and correlations are presented in Table 1 . Pearson correlations showed small to moderate correlations between the variables as an exception of sleep disturbances and respiratory infections which were not correlated neither at Time 1 nor at Time 2 and headaches at Time 1 and respiratory infections at Time 2. Table 2 presents the TEGRM model parameters and odds ratio. Relevant to the first hypothesis, the main effects of the TERGM model informed about the incoming relationships. The results indicated that sleep disturbances and respiratory infections were negatively related to being perceived as an optimist, as opposed to those with less sleep disturbances and respiratory infections. Moreover, the interaction effect between time and respiratory infections of the potential receiver was significant and negative. This means that individuals scoring higher on respiratory infections were also perceived as less optimistic over time. Figure 1 shows that individuals with respiratory infections received less optimism nominations over time. The rest of self-reported physical health symptoms were unrelated to nominations of optimism. Table 1 Descriptive statistics and Pearson correlations M SD 1 2 3 4 5 6 7 1. Sleep Time 1 3.07 1.24 2. Head Time 1 3.72 1.50 .43 ** 3. Gastro Time 1 2.66 1.29 .32 ** .42 ** 4. Respiratory Time 1 2.70 1.12 .00 .16 * .23 ** 5. Sleep Time 2 3.15 1.31 .64 ** .27 ** .21 ** .09 6. Head Time 2 3.65 1.59 .42 ** .66 ** .32 ** .13 * .41 ** 7. Gastro Time 2 2.67 1.30 .27 ** .27 ** .60 ** .14 * .33 ** .35 ** 8. Respiratory Time 2 2.31 0.89 .09 .10 .16* .30 ** .17 ** .19 ** .22 ** Note : ** = p < 0.01; * = p < 0.05 Exogenous Control Variables For the exogenous variables, we included the outgoing relationships (i.e., nominating others as optimists). The results showed that individuals scoring higher on sleep disturbances and respiratory infections tended to nominate less peers as optimistic at Time 1. However, individuals presenting sleep disturbances and respiratory infections tended to nominate more peers as optimistic at Time 2. The significant positive exogenous effects associated with the term “Time period” for headaches, gastrointestinal problems, and respiratory infections suggest that individuals experiencing higher levels of these symptoms became increasingly likely to nominate others as optimistic between the first and second measurement assessments. Notably, this effect was most pronounced for respiratory infections. Additionally, the negative coefficients for the “Sex: node match” term across all variables indicate a tendency for individuals to nominate others of the opposite sex as optimistic. The “Absdiff” term informs about the differences in a variable between the value of the potential sender of a nomination and the potential receiver. For instance, a negative effect of the “Absdiff” term in the sleep disturbances variable suggests that greater differences in sleep disturbances between individuals were associated with a lower likelihood of nominating each other as optimistic. Table 2 TERGM estimates and odds ratio TERGM ( SE ) OR 95% CI Main effects Sleep: Receiver -0.07 (0.06) * 0.92 [0.81–1.04] Sleep: Receiver X Time -0.00 (0.03) 0.99 [0.92–1.06] Head: Receiver -0.07 (0.05) 0.92 [0.83–1.01] Head: Receiver X Time -0.02 (0.03) 0.97 [0.92–1.03] Gastro: Receiver 0.02 (0.05) 1.03 [0.92–1.16] Gastro: Receiver X Time -0.05 (0.03) 0.94 [0.87-1.00] Respiratory: Receiver -0.14 (0.07) * 1.16 [1.00-1.33] Respiratory: Receiver X Time -0.16 (0.04) *** 0.84 [0.76–0.92] Exogenous control variables Sleep: Time period 0.12 (0.14) 1.13 [0.84–1.52] Sleep: Sender -0.09 (0.05) 0.90 [0.81–1.01] Sleep: Sender X Time 0.07 (0.03) * 1.07 [1.00-1.15] Sleep: Sex node match -0.11 (0.04) * 0.87 [0.77–0.99] Sleep: Absdiff 0.06 (0.03) * 1.06 [1.00-1.13] Head: Time period 0.32 (0.14) * 1.37 [1.02–1.83] Head: Sender -0.03 (0.04) 0.97 [0.88–1.06] Head: Sender X Time 0.02 (0.02) 1.03 [0.96–1.08] Head: Sex node match -0.15 (0.07) * 0.85 [0.74–0.98] Head: Absdiff 0.01(0.02) 1.01 [0.96–1.07] Gastro: Time period 0.42 (0.12) *** 1.55 [1.20-2.00] Gastro: Sender -0.03 (0.05) 0.96 [0.86–1.07] Gastro: Sender X Time 0.01 (0.03) 1.02 [0.95–1.08] Gastro: Sex node match -0.16 (0.07) * 0.84 [0.73–0.97] Gastro: Absdiff 0.02 (0.03) 1.02 [0.96–1.08] Respiratory: Time period 1.09 (0.15) *** 3.05 [2.27–4.09] Respiratory: Sender -0.23 (0.06) *** 1.27 [1.12–1.44] Respiratory: Sender X Time 0.15 (0.04) *** 0.85 [0.78–0.93] Respiratory: Sex node match -0.20 (0.07) ** 0.81 [0.70–0.93] Respiratory: Absdiff -0.03 (0.03) 0.96 [0.89–1.04] Endogenous network dependencies Sleep: Edges -1.19 (0.25) *** 0.28 [0.16–0.48] Sleep: Reciprocity 0.51 (0.06) *** 1.69 [1.48–1.92] Sleep: GWESP 0.29 (0.06) *** 1.35 [1.19–1.53] Sleep: GWODEG -2.42 (0.23) *** 0.09 [0.05–0.14] Head: Edges -1.37 (0.26) *** 0.25 [0.15–0.42] Head: Reciprocity 0.51 (0.06) *** 1.67 [1.45–1.91] Head: GWESP 0.29 (0.06) *** 1.34 [1.18–1.53] Head: GWODEG -2.50 (0.23) *** 0.07 [0.05–0.12] Gastro: Edges -1.76 (0.22) *** 0.16 [0.10–0.25] Gastro: Reciprocity 0.51 (0.06) *** 1.66 [1.45–1.90] Gastro: GWESP 0.30 (0.06) *** 1.35 [1.19–1.53] Gastro: GWODegree -2.40 (0.24) *** 0.08 [0.05–0.13] Respiratory: Edges -2.74 (0.25) *** 0.06 [0.03–0.10] Respiratory: Reciprocity 0.50 (0.06) *** 1.66 [1.44–1.90] Respiratory: GWESP 0.31 (0.06) *** 1.36 [1.21–1.54] Respiratory: GWODEG -2.36 (0.23) *** 0.09 [0.06–0.15] Note : *** p = 0; ** = p < 0.001; * = p < 0.05 Main effect of respiratory symptoms predicting being nominated as optimistic. The effect for Time 1 is presented in a black line and for Time 2 in a dark gray line. The light grey line represents the main effect irrespective of time. The shaded areas indicate 95% confidence intervals. Endogenous Network Dependencies The significant reciprocity term indicated that optimism nominations were more mutual than expected by chance in all variables. The significant GWESP effect implied that individuals who nominated others as optimistic, their nominated peers also perceived others as optimistic, showing a tendency to create triads. The GWODEG term indicated that some people had generally lower thresholds of perceiving others as optimists. Goodness-of-Fit Assessment To examine the quality of TERGM fit, we conducted a test to simulate one hundred new networks based on the model parameters and covariates and compared those with the networks observed from the data of the present study. The distributions of the new networks matched the observed distributions of the same statistics well enough suggesting that the estimated TERGMs of the study are well fitted to the data. Discussion The present study aimed to uncover the extent to which physical health symptoms predicted perceptions of optimism in freshmen using the SNA approach with a multilevel design. By combining self-reported and peer-reported data, our research provides method-independent insights into how physical health symptoms intertwines with optimism within a social context, specifically within university classrooms. Notably, sleep disturbances and respiratory infections were significant predictors of optimism perceptions, with individuals experiencing these health issues being less likely to be seen as optimistic by their peers. Furthermore, the impact of respiratory infections on optimism perceptions persisted over time, highlighting the long-term social implications of physical health on interpersonal judgments. Incoming Relationships: Which Physical Health Symptoms Predict Being Perceived as Optimistic? The models examining the relationship between physical health symptoms and perceptions of optimism revealed compelling findings. Individuals experiencing sleep disturbances and respiratory infections were consistently perceived as less optimistic by their peers. The association between these two health conditions and optimism has been more extensively documented in empirical research than headaches and gastrointestinal problems (Haack & Mullington, 2005 ; Koo et al., 2022 ; Lemola et al., 2013 ). Prior studies have shown that sleep disturbances are linked to a more pessimistic outlook (Hernandez et al., 2020 ). Our findings align with this evidence, further suggesting that individuals suffering from sleep disturbances may not only experience a pessimistic view themselves but also be perceived by others as less optimistic. Sleep disruptions can significantly impact emotional well-being by increasing stress, fatigue, and discomfort, all of which contribute to a diminished optimistic outlook (Lau et al., 2015 ). Similarly, respiratory infections have been previously associated with a less optimistic outlook (Koo et al., 2022 ). Consistent with these findings, our results extend this understanding by suggesting that not only do individuals experiencing respiratory infections exhibit lower optimism, but they are also perceived by others as less optimistic. This may be due to the visible physical symptoms of illness, such as fatigue, congestion, and discomfort, which could lead observers to associate these individuals with lower levels of positivity. Given that social perception is influenced by nonverbal cues and overall well-being, the sustained effect of respiratory infections on optimism perception over time suggests a deeper connection between physical health and social judgments of optimism. Our study highlights the formative role of social connections in shaping perceptions of optimism. Previous research has established a link between self-reported health issues and a more pessimistic outlook, and our findings suggest that this relationship may extend to peer-reported perceptions as well. Social relationships serve as channels for transmitting attitudes, emotions, and even health-related information (Christakis & Fowler, 2009 ; Fowler & Christakis, 2008 ), which can, in turn, shape how individuals are perceived within their social networks. These findings feature the possibility that one’s physical health not only influences personal emotional states but also affects how others perceive and attribute subjective characteristics, such as optimism. Observable health symptoms may lead to implicit biases in social interactions, influencing judgments about an individual’s disposition and potentially altering social dynamics over time. This suggests that social perceptions of optimism are not formed in isolation but are shaped by both individual health status and broader interpersonal influences. Outgoing Relationships: Which Physical Health Symptoms Predict Perceiving Others as Optimistic? Our models revealed a noteworthy pattern in the relationship between physical health symptoms and perceptions of optimism. At Time 1, individuals experiencing sleep disturbances and respiratory infections were less likely to perceive their peers as optimistic. However, this trend shifted at Time 2, where individuals with respiratory infections were more likely to perceive others as optimistic. A plausible explanation for this reversal lies in the evolving dynamics of social interactions. During the initial stages of social integration, individuals experiencing physical health symptoms may have had limited social engagement, leading to fewer opportunities to form positive perceptions of their peers. Physical discomfort, fatigue, or social withdrawal associated with these symptoms could have contributed to an initial pessimistic bias. However, as time progressed and these individuals became more socially integrated, their increased exposure to and engagement with peers may have provided them with richer social information, enabling them to develop more positive perceptions of others (Cohen, 2004 ). This shift emphasizes the importance of considering the temporal and contextual aspects of social perception, particularly in relation to physical health. Future research should further explore the underlying mechanisms driving this change, such as increased familiarity, changes in emotional states, or the role of supportive social interactions in shaping optimism perceptions. Strengths and limitations Among the strengths of this study are its sample size, longitudinal design, perspective for integrating actor and partner reports, and statistical approach. However, the interpretations are limited to undergraduates from a Western, educated, industrialized, and democratic nation. Studies featuring samples with diverse demographic and cultural characteristics are necessary to ensure the robustness of these findings (Henrich et al., 2010 ). The naturalistic setting adopted did not allow for explicit control over how, when, and how frequently the participants interacted during the investigation, which might have shed light about important conditions under which this influence occurred. Future studies could offer valuable insights into this topic by controlling for these variables. Ultimately, future studies could incorporate additional measurement time points and reduce the intervals between measurements to track changes in zero-acquaintance relationships as time progresses (Beer, 2021 ). Conclusions This study offers valuable insights into how physical health symptoms shape social perceptions of optimism within university classrooms. Our findings show that sleep disturbances and respiratory infections led individuals to be perceived as less optimistic and to perceive others similarly at Time 1. At Time 2, individuals with respiratory infections continued to be seen as less optimistic, while their own perceptions of others shifted toward greater optimism. This suggests that social exposure and integration over time may either maintain or alter perceptions of optimism. Our results expand existing literature by emphasizing the role of social judgments within peer networks, going beyond self-reported experiences of optimism. Given the link between optimism and qualities such as social desirability, leadership, and emotional resilience, biased perceptions of individuals with health symptoms could lead to social exclusion and reduced support. This, in turn, may negatively affect emotional well-being and hinder social integration, particularly in university settings where peer relationships play a key role. The study highlights the importance of social support in health outcomes, as biased perceptions can limit access to emotional and practical support. To improve perceptions and outcomes for individuals facing physical health challenges, addressing these biases through educational initiatives, social interventions, and healthcare policies is necessary. Declarations Ethics approval and consent to participate This study was approved by the Standards Committee of the Faculty of Education, Psychology and Social Work, University of Lleida and is in accordance with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. All participants were informed about the research and gave explicit consent. Consent for publication Not applicable Availability of data and materials The data and code associated with the study are available at Open Science Framework: https://osf.io/82rk4/?view_only=c5d09d74f36f4e41a4bb2a154cdbc5b6 Competing interests The authors declare that there is no conflict of interests to disclose. Funding This research received no funding Authors' contributions CTG: Conceptualization, Methodology, Data curation, Writing - Original draft preparation. ABB: Conceptualization, Methodology, Writing - Reviewing. CA: Visualization, Supervision. Acknowledgments Not applicable References Andersson MA. Dispositional optimism and the emergence of social network diversity. Sociol Q. 2012;53(1):92–115. https://doi.org/10.1111/j.1533-8525.2011.01227.x . Back MD, Vazire S. The social consequences of personality: Six suggestions for future research. Eur J Pers. 2015;29(2):296–307. https://doi.org/10.1002/per.1998 . Beer A. Information as a moderator of accuracy in personality judgment. In: Letzring TD, Spain JS, editors. The Oxford handbook of accurate personality judgment. Oxford University Press; 2021. pp. 132–48. Beiter R, Nash R, McCrady M, Rhoades D, Linscomb M, Clarahan M, Sammut S. The prevalence and correlates of depression, anxiety, and stress in a sample of college students. 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Heinrich LM, Gullone E. The clinical significance of loneliness: A literature review. Clin Psychol Rev. 2006;26(6):695–718. https://doi.org/10.1016/j.cpr.2006.04.002 . Henrich J, Heine SJ, Norenzayan A. Most people are not WEIRD. Nature. 2010;466(29). https://doi.org/10.1038/466029a . Hinz A, Ernst J, Glaesmer H, Brähler E, Rauscher FG, Petrowski K, Kocalevent RD. Frequency of somatic symptoms in the general population: Normative values for the Patient Health Questionnaire-15 (PHQ-15). J Psychosom Res. 2017;96:27–31. https://doi.org/10.1016/j.jpsychores.2016.12.017 . Katon JW. Epidemiology and treatment of depression in patients with chronic medical illness. Dialog Clin Neurosci. 2011;13(1):7–23. https://doi.org/10.31887/DCNS.2011.13.1/wkaton . Kirmayer LJ, Groleau D, Looper KJ, Dao MD. Explaining medically unexplained symptoms. Can J Psychiatry. 2004;49(10):663–72. https://doi.org/10.1177/0706743704049010 . Knoke D, Yang S. Social network analysis. Sage; 2008. p. 154. Koo HK, Hoth KF, Make BJ, Regan EA, Crapo JD, Silverman EK, DeMeo DL. Optimism is associated with respiratory symptoms and functional status in chronic obstructive pulmonary disease. Respir Res. 2022;23(1):19. https://doi.org/10.1186/s12931-021-01922-6 . Kroenke KMD. The interface between physical and psychological symptoms. J Clin Psychiatry. 2003;5(7):11–8. Hernandez R, Vu THT, Kershaw KN, Boehm JK, Kubzansky LD, Carnethon M, Trudel-Fitzgerald C, Knutson K, Colangelo LA, Liu K. The association of optimism with sleep duration and quality: Findings from the coronary artery risk and development in young adults (CARDIA) study. Behav Med. 2020;46(2):100–11. https://doi.org/10.1080/08964289.2019.1575179 . Lau EYY, Hui CH, Cheung SF, Lam J. Bidirectional relationship between sleep and optimism with depressive mood as a mediator: a longitudinal study of Chinese working adults. J Psychosom Res. 2015;79(5):428–34. https://doi.org/10.1016/j.jpsychores.2015.09.010 . Lemola S, Räikkönen K, Gomez V, Allemand M. Optimism and self-esteem are related to sleep. Results from a large community-based sample. Int J Behav Med. 2013;20:567–71. https://doi.org/10.1007/S12529-012-9272-Z . Lyubomirsky S, King L, Diener E. The benefits of frequent positive affect: Does happiness lead to success? Psychol Bull. 2005;131(6):803. Nestler S, Grimm KJ, Schönbrodt FD. The social consequences and mechanisms of personality: How to analyse longitudinal data from individual, dyadic, round-robin and network designs. Eur J Pers. 2015;29(2):272–95. https://doi.org/10.1002/PER.1997 . Pascoe MC, Hetrick SE, Parker AG. The impact of stress on students in secondary school and higher education. Int J Adolescence Youth. 2020;25(1):104–12. https://doi.org/10.1080/02673843.2019.1596823 . Poulin F, Boivin M. The role of proactive and reactive aggression in the formation and development of boys' friendships. Dev Psychol. 2000;36(2):233. https://doi.org/10.1037/0012-1649.36.2.233 . Rasmussen HN, Scheier MF, Greenhouse JB. Optimism and physical health: A meta-analytic review. Ann Behav Med. 2009;37(3):239–56. https://doi.org/10.1007/s12160-009-9111-x . Schat AC, Kelloway EK, Desmarais S. The Physical Health Questionnaire (PHQ): Construct validation of a self-report scale of somatic symptoms. J Occup Health Psychol. 2005;10(4):363–81. https://doi.org/10.1037/1076-8998.10.4.363 . Scheier MF, Carver CS. Optimism, coping, and health: assessment and implications of generalized outcome expectancies. Health Psychol. 1985;4(3):219–47. Scheier MF, Carver CS. Dispositional optimism and physical health: A long look back, a quick look forward. Am Psychol. 2018;73(9):1082. Scott J. Social Network Analysis. Sociology. 1988;22(1):109–27. https://doi.org/10.1177/0038038588022001007 . Scott J. Social Network Analysis. A Handbook. Cambridge University Press; 2000. https://doi.org/10.4135/9781529716597 . Segerstrom SC, Carver CS, Scheier MF. (2017). Optimism. The happy mind: Cognitive contributions to well-being , 195–212. https://doi.org/10.1007/978-3-319-58763-9_11 Steglich C, Snijders TAB, Pearson M. Dynamic networks and behavior: Separating selection from influence. Sociol Methodol. 2010;40(1):329–93. https://doi.org/10.1111/J.1467-9531.2010.01225.X . Tejada-Gallardo C, Blasco-Belled A, Alsinet C. Does mental well-being predict being perceived as a happy peer? A longitudinal social network study. Pers Indiv Differ. 2023;202:111988. https://doi.org/10.1016/j.paid.2022.111988 . Vazire S. Who knows what about a person? The Self-Other Knowledge Asymmetry (SOKA) model. J Personal Soc Psychol. 2010;98(2):281–300. https://doi.org/10.1037/a0017908 . Wasserman S, Faust K. Social Network Analysis: Methods and Applications. Cambridge University Press; 1994. https://doi.org/10.1017/CBO9780511815478 . Wölfer R, Faber NS, Hewstone M. Social network analysis in the science of groups: Cross-sectional and longitudinal applications for studying intra- and intergroup behavior. Group Dynamics: Theory Res Pract. 2015;19(1):45–61. https://doi.org/10.1037/GDN0000021 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 23 Jun, 2025 Reviewers agreed at journal 13 Jun, 2025 Reviewers invited by journal 11 Jun, 2025 Editor assigned by journal 14 Apr, 2025 Editor invited by journal 28 Mar, 2025 Submission checks completed at journal 28 Mar, 2025 First submitted to journal 28 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6310226","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":469959782,"identity":"0f34f009-98f5-494b-b1e2-f68a9eb8b971","order_by":0,"name":"Claudia Tejada-Gallardo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/0lEQVRIiWNgGAWjYDACdiDmMQAzExh4GGwYGI4T0sIM1cID0ZLGwHAYop2AFigGEocJa+FvZn4m8abAjsGeveHhhzc15xP7DjMwfvj5A7cWicNsZpJzDJIZeHgOJEvOOXY7ceZhBmbJHjy2GDAzmEnzAEkeiYQEaR6224kbDjOwMfDg1cL+DailnoFH/kHyb55/58BaGP/g1cIDsuUw0BaGNGnetgNgLcz4bJE4zFNsOcfgOA/PmYQ0y7l9ycYzDzM2S8uk4dbC396+8cabP9Vy7O1nkm+8+WYn23e8+eDHNza4tcAAMFrgjmFsIKweAtgPEKtyFIyCUTAKRhgAAJbWSTk8td/hAAAAAElFTkSuQmCC","orcid":"","institution":"University of Lleida","correspondingAuthor":true,"prefix":"","firstName":"Claudia","middleName":"","lastName":"Tejada-Gallardo","suffix":""},{"id":469959783,"identity":"019f1745-1d99-4207-9d38-bd8da76315f3","order_by":1,"name":"Ana Blasco-Belled","email":"","orcid":"","institution":"University of Lleida","correspondingAuthor":false,"prefix":"","firstName":"Ana","middleName":"","lastName":"Blasco-Belled","suffix":""},{"id":469959784,"identity":"475c9eb6-d5ff-4086-b5c4-1ce125ce57dc","order_by":2,"name":"Carles Alsinet","email":"","orcid":"","institution":"University of Lleida","correspondingAuthor":false,"prefix":"","firstName":"Carles","middleName":"","lastName":"Alsinet","suffix":""}],"badges":[],"createdAt":"2025-03-26 08:23:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6310226/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6310226/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84689746,"identity":"74932092-d339-489d-90d1-d267070d6ca6","added_by":"auto","created_at":"2025-06-16 09:32:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":10949,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eMain effect of respiratory symptoms predicting being nominated as optimistic. The effect for Time 1 is presented in a black line and for Time 2 in a dark gray line. The light grey line represents the main effect irrespective of time. The shaded areas indicate 95% confidence intervals.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6310226/v1/3221d6ea8a2c1f1e629d559b.png"},{"id":84692034,"identity":"37256265-1aef-47b9-a0e8-eb20a94d248a","added_by":"auto","created_at":"2025-06-16 09:56:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":938234,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6310226/v1/4f3b1b21-e34a-4b4b-ab3b-83c8566dfe27.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"How Does Physical Health Influence the Perception of Optimism? A Longitudinal Social Network Analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePersonality research has been interested in understanding how individual differences influence the broader social environment (Fowler et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). This line of inquiry has led to the examination of personality traits in interaction with social structures, encompassing the ways individuals navigate and integrate within social groups. In recent years, optimism has emerged as a key psychological trait of interest in this context. Defined as a general positive expectation about the future, optimism has been linked to numerous beneficial outcomes, including physical health (Scheier \u0026amp; Carver, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1985\u003c/span\u003e; Scheier \u0026amp; Carver, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Extensive research indicates that individuals with higher levels of optimism are more likely to experience better overall health and recover more quickly from illness and medical procedures (Segerstrom et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). However, the inverse relationship\u0026mdash;how physical health influences the perception of optimism, particularly within a social context\u0026mdash;remains unexplored. Understanding this dynamic is needed for exploring the role of physical health in shaping individuals\u0026rsquo; perceptions of optimism within a social context during the development of social ties.\u003c/p\u003e \u003cp\u003eOptimism plays a role in the formation of social relationships, with several empirical evidence providing account for its contribution to social network ties and diversity. For instance, individuals who maintain an optimistic outlook towards the future possess larger, stronger, and more diverse social networks (Poulin \u0026amp; Boivin, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Lyubomirsky et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Maintaining optimistic expectations about the future may motivate individuals to seek out new connections and engage in social activities, increasing the likelihood of forming meaningful ties with other people (Heinrich \u0026amp; Gullone, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Furthermore, optimism might contribute to social resilience, as optimistic individuals are more likely to nurture their social ties and navigate challenging situations during adverse circumstances (Fredrickson, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA suitable framework for exploring the influence of optimism on social structures is to adopt a biosocial perspective that encompasses both biological and structural (i.e., social) mechanisms (Andersson, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Within this framework, structural mechanisms involve the social structures in which individuals navigate, such as classrooms or neighborhoods, while biological mechanisms encompass genetic predispositions and physiological processes, including the manifestation of physical health symptoms (Freese, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). In social relationships, besides the transmission of emotions and attitudes, an individual\u0026rsquo;s physical health can also be noticed (Christakis \u0026amp; Fowler, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Despite the biosocial perspective emphasizes the relevant role of the social context in the interaction between biological and psychological outcomes (Freese, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), it is a question that remains underexplored. Hence, recognizing the interplay between biological factors and social structures offers a more nuanced and realistic understanding of how optimism and social networks interact (Andersson, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eThe Role of Physical Health Symptoms in Being Perceived as Optimistic\u003c/h3\u003e\n\u003cp\u003ePhysical health symptoms refer to physical sensations or experiences, which can range from mild to severe, that may be indicative of an underlying health condition or illness (Katon, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Physical health symptoms account for over half of all outpatient visits, from which approximately half of these include pain complaints (e.g., headaches), respiratory problems (e.g., cough, sore or throat), and non-specific symptoms (e.g., insomnia; Kroenke, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). The presence of physical health symptoms has an impact on individuals' overall well-being, affecting their emotional, social, and general physical health (Rasmussen et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Individuals who experience these symptoms continuously exhibit a variety of psychopathological manifestations, including depression, anxiety, and impaired quality of life (Escobar et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Hinz et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe effects of physical health symptoms can also interfere with the interpersonal sphere (Kirmayer et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) by influencing the way individuals interact and form connections with others. In this process, optimism might play an important role given that negative attributions about one\u0026rsquo;s health status, such as poor health habits or chronic illness, are related to unfavorable perceptions of optimism (Conversano et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). When examining the influence of social interactions in the perception of optimism, methodological nuances need to be considered. For example, relying on self-reported data entails some limitations, such as missing the opportunity of incorporating relevant information from contextual sources that are contemplated within the biosocial model (e.g., peer-based reports), and providing scores influenced by social desirability and recall biases (Knoke \u0026amp; Yang, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). To overcome these limitations, both self- and peer-based reports should be included. For example, one could examine the influence of self-reported physical health symptoms in being perceived as an optimistic person, which is the aim of the current study. To do that, the social network analysis offers a suitable methodological framework to investigate questions including self- and peer-reported data (Fowler \u0026amp; Christakis, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eThe Social Network Analysis\u003c/h2\u003e \u003cp\u003eThe social network analysis focuses on studying the patterns of interpersonal relationships among individuals forming a network of ties (Scott, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1988\u003c/span\u003e). This approach offers a comprehensive perspective by integrating both self-reports and peer-nominations, providing several methodological advantages. By considering both self-rated physical health symptoms and peer-rated evaluations of optimism, we can overcome limitations related to incomplete self-knowledge and biased reporting (Back \u0026amp; Vazire, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Vazire, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). This approach allows to investigate whether self-reported physical health symptoms can predict peer-rated nominations without the unwanted influence of method variance (Funder, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). As social network approach presents a valuable opportunity to investigate longitudinal changes in social networks (e.g., do other people see me as an optimistic person?) and explore the influence of biological factors on these dynamics (e.g., do these attributions depend on my physical health symptoms?) (Nestler et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; W\u0026ouml;lfer et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), we employed this methodological approach to examine how physical health symptoms influence optimism from a peer-based perspective. A detailed explanation of this approach is presented in the Methodology section.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCurrent Study\u003c/h3\u003e\n\u003cp\u003eThe goal of the present study is to investigate the interplay between self-reported physical health symptoms and peer-based perceptions of optimism. More concretely, we will examine the extent to which self-reported physical health symptoms influence peer-reported perceptions of optimism (i.e., how does my physical health symptoms influence whether my peers see me as an optimistic person?) and its temporal evolution (i.e., do these attributions change over time?) in a given social structure (i.e., classrooms). The study included two measurement assessments, with the first measurement taking place the beginning of the first academic year, and the second taking place seven months later. Based on the premise that physical health issues can often lead to a less optimistic outlook (e.g., Rasmussen et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), we expect that self-reported physical health symptoms would negatively predict being perceived as an optimistic person by peers in the initial assessment (H1), and that this negative association would be maintained at the second assessment (H2).\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eThe present study comprised 15 groups of first-year university students aged between 18 and 32 (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;19.31, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.78, 75.8% females) from the University of Lleida (Spain). Undergraduates are a suitable target for this research because they need to adapt to a new social environment, where social (e.g., making new acquaintances) and academic challenges (e.g., adapting to new academic standards) can precede physical health symptoms (Beiter et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Pascoe et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). One of the initial groups did not complete the second measurement assessment, therefore the final number of networks included were 14 each time, resulting in a total of 28 networks. Some participants were removed from the study because either responded wrongly to attention-check items (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;100) or did not complete the two measurement assessments (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;35). As a result, the final retained sample for the study included 240 individuals. The retention rate over the seven months was 58.5%. The participants were informed of the study goals and procedure, enrolled voluntarily in the study, and signed an informed consent allowing their data to be used anonymously for research purposes.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eProcedure\u003c/h3\u003e\n\u003cp\u003eParticipants completed the two measurement assessments in classes. However, two out of the 28 groups completed it online at Time 1 and Time 2 due to COVID-19 restrictions. As first-year students, participants were mostly new to each other at Time 1. In contrast, until the measurement was repeated at Time 2, they had the chance to meet in class and interact during the rest of the semester. Even though restrictions due to COVID-19 were still present, the majority of groups assisted to face-to-face classes. Participants had the opportunity to freely interact with each other throughout the course and no instructions nor restrictions were provided about the way they could interact, leaving the formation of ties and group dynamics evolve within a naturalistic setting.\u003c/p\u003e \u003cp\u003eTo collect the data, we provided the participants with a Google Forms link containing the self-reports of physical health symptoms and a pencil-and-paper sheet including the full name of all the members of the class for the nomination task. After completing the self-reports, the participants had to mark those peers who in their opinion were optimistic. Participants were allowed to select as many peers as they wished, except themselves. This study was approved by the committee of the faculty of education, psychology and social work of the University of Lleida (Spain).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eInstruments\u003c/h2\u003e \u003cp\u003ePhysical health symptoms were assessed with the Physical Health Questionnaire (PHQ; Schat et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) translated and back translated into Spanish. The scale included 14 items measuring the frequency with which respondents experienced four physical health problems: sleep disturbances, headaches, respiratory infections, and gastrointestinal problems. Items 1\u0026ndash;11 were rated on a 7-point Likert scale ranging from 1 (\u003cem\u003enot at all\u003c/em\u003e) to 7 (\u003cem\u003eall of the time\u003c/em\u003e). Items 12 and 13 were rated from 0 (\u003cem\u003ezero times\u003c/em\u003e) to 7 (\u003cem\u003eseven or more times\u003c/em\u003e). An item example is \u0026ldquo;How many times have you had minor colds?\u0026rdquo;. Item 14 \u0026ldquo;When you had a bad cold or flu, how long did it typically last?\u0026rdquo;, was rated from 1 (\u003cem\u003eone day\u003c/em\u003e) to 7 (\u003cem\u003eseven or more days\u003c/em\u003e). Internal reliability coefficients (Cronbach\u0026rsquo;s alpha) at Time 1 were .82 for sleep disturbances, .85 for headaches, .79 for gastrointestinal problems, and .63 for respiratory infections. At Time 2 the internal reliability coefficients were .78 for sleep disturbances, .85 for headaches, .80 for gastrointestinal problems, and .60 for respiratory infections.\u003c/p\u003e \u003cp\u003eSocial networks were assessed with a pencil-and-paper task for peer nominations. At each measurement assessment, students were presented with a list of all peers\u0026rsquo; names and were asked to select the classmates they considered to be optimistic. The selected individuals were coded as 1 (being nominated) and 0 (being non-nominated).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eThe present study employed a multilevel design consisting of multiple time assessments and multiple groups. The network-analytic approach requires no missing values to enter networks as independent and progressive temporal steps to capture the time dependency effect, that is, whether there were differences in how a psychological variable (i.e., optimism) influenced peer nominations as time progressed. Therefore, only data from the participants who completed the two measurement assessments were included in the analyses. This approach has proven effective in previous research (Tejada-Gallardo et al., 2022).\u003c/p\u003e \u003cp\u003eTo analyze the dynamics of social networks, we used the social network analysis in RStudio (RStudio Team, 2020). We applied the exponential-family random graph model (ERGM) and its extension for longitudinal data, the temporal exponential-family random graph model (TERGM). Both ERGM and TERGM represent a general class of models that allow simulating a pattern of dependencies between a set of covariates and participants\u0026rsquo; relationships within a social network. The ERGM lies on the assumption that independency cannot be met because the scores of participants among a delimited group are not independent of those of the rest of the participants. Hence, multiple dependent values were collected from each participant, which required the use of analytic methods suitable for dependent network data (within a group). TERGM was also suitable for this procedure because it allowed to include independent networks (i.e., 14 independent classes) and, at the same time, time dependency (i.e., two time points).\u003c/p\u003e \u003cp\u003eThe models employed in this study are used to study the relationships within a group (i.e., network). These relationships can depend on internal (endogenous) or external (exogenous) covariates. Exogenous covariates do not directly rely on the internal mechanism that create the network. In our study, the exogenous covariate was optimism, and it was used as an individual characteristic of the participants treated from an ego (sender: influence in nominating) and alter (receiver: influence in being nominated) perspective. It is important to evaluate certain universal dependencies according to the general network theory (Scott, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Wasserman \u0026amp; Faust, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1994\u003c/span\u003e), which are included in the analyzed model (Wasserman \u0026amp; Faust, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). The common tendency to reciprocate nominations within a network was measured using a reciprocity parameter (mutual term), which controls for the overestimated selection effects of reciprocating relationships. The tendency to create triads was measured using a geometrically weighted edgewise shared partner (GWESP) distribution term. Generally speaking, in a triadic relationship, the transitivity effect is used to control for the overestimated selection effects (e.g., those individuals who become friends with their friends\u0026rsquo; friends; Steglich et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). We also introduced the geometrically weighted out-degree (GWODEG) distribution term to control for the tendency to select almost everyone. Finally, we included an absolute difference term (absdiff) among the optimism scores for every interaction in each group. If significant, this estimate suggests that the absolute differences of optimism might influence the likelihood of a relationship between two participants.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eMain Effects: The Influence of Physical Health Symptoms on Perceived Optimism\u003c/h2\u003e \u003cp\u003eDescriptive statistics and correlations are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Pearson correlations showed small to moderate correlations between the variables as an exception of sleep disturbances and respiratory infections which were not correlated neither at Time 1 nor at Time 2 and headaches at Time 1 and respiratory infections at Time 2. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the TEGRM model parameters and odds ratio. Relevant to the first hypothesis, the main effects of the TERGM model informed about the incoming relationships. The results indicated that sleep disturbances and respiratory infections were negatively related to being perceived as an optimist, as opposed to those with less sleep disturbances and respiratory infections. Moreover, the interaction effect between time and respiratory infections of the potential receiver was significant and negative. This means that individuals scoring higher on respiratory infections were also perceived as less optimistic over time. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows that individuals with respiratory infections received less optimism nominations over time. The rest of self-reported physical health symptoms were unrelated to nominations of optimism.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eDescriptive statistics and Pearson correlations\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eM\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1. Sleep Time 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2. Head Time 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.43\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3. Gastro Time 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.32\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.42\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4. Respiratory Time 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.16\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.23\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5. Sleep Time 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.64\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.27\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.21\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6. Head Time 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.42\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.66\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.32\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.13\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.41\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7. Gastro Time 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.27\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.27\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.60\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.14\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.33\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.35\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8. Respiratory Time 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.16*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.30\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.17\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.19\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.22\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003cem\u003eNote\u003c/em\u003e: \u003csup\u003e**\u003c/sup\u003e= \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01; \u003csup\u003e*\u003c/sup\u003e = \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eExogenous Control Variables\u003c/h2\u003e \u003cp\u003eFor the exogenous variables, we included the outgoing relationships (i.e., nominating others as optimists). The results showed that individuals scoring higher on sleep disturbances and respiratory infections tended to nominate less peers as optimistic at Time 1. However, individuals presenting sleep disturbances and respiratory infections tended to nominate more peers as optimistic at Time 2.\u003c/p\u003e \u003cp\u003eThe significant positive exogenous effects associated with the term \u0026ldquo;Time period\u0026rdquo; for headaches, gastrointestinal problems, and respiratory infections suggest that individuals experiencing higher levels of these symptoms became increasingly likely to nominate others as optimistic between the first and second measurement assessments. Notably, this effect was most pronounced for respiratory infections. Additionally, the negative coefficients for the \u0026ldquo;Sex: node match\u0026rdquo; term across all variables indicate a tendency for individuals to nominate others of the opposite sex as optimistic. The \u0026ldquo;Absdiff\u0026rdquo; term informs about the differences in a variable between the value of the potential sender of a nomination and the potential receiver. For instance, a negative effect of the \u0026ldquo;Absdiff\u0026rdquo; term in the sleep disturbances variable suggests that greater differences in sleep disturbances between individuals were associated with a lower likelihood of nominating each other as optimistic.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eTERGM estimates and odds ratio\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTERGM (\u003cem\u003eSE\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMain effects\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep: Receiver\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.07 (0.06)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e[0.81\u0026ndash;1.04]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep: Receiver X Time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.00 (0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e[0.92\u0026ndash;1.06]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHead: Receiver\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.07 (0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e[0.83\u0026ndash;1.01]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHead: Receiver X Time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.02 (0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e[0.92\u0026ndash;1.03]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGastro: Receiver\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.02 (0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e[0.92\u0026ndash;1.16]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGastro: Receiver X Time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.05 (0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e[0.87-1.00]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespiratory: Receiver\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.14 (0.07)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e[1.00-1.33]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespiratory: Receiver X Time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.16 (0.04)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e[0.76\u0026ndash;0.92]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eExogenous control variables\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep: Time period\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.12 (0.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e[0.84\u0026ndash;1.52]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep: Sender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.09 (0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e[0.81\u0026ndash;1.01]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep: Sender X Time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.07 (0.03)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e[1.00-1.15]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep: Sex node match\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.11 (0.04)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e[0.77\u0026ndash;0.99]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep: Absdiff\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.06 (0.03)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e[1.00-1.13]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHead: Time period\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.32 (0.14)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e[1.02\u0026ndash;1.83]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHead: Sender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.03 (0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e[0.88\u0026ndash;1.06]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHead: Sender X Time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.02 (0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e[0.96\u0026ndash;1.08]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHead: Sex node match\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.15 (0.07)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e[0.74\u0026ndash;0.98]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHead: Absdiff\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.01(0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e[0.96\u0026ndash;1.07]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGastro: Time period\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.42 (0.12)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e[1.20-2.00]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGastro: Sender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.03 (0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e[0.86\u0026ndash;1.07]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGastro: Sender X Time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.01 (0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e[0.95\u0026ndash;1.08]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGastro: Sex node match\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.16 (0.07)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e[0.73\u0026ndash;0.97]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGastro: Absdiff\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.02 (0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e[0.96\u0026ndash;1.08]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespiratory: Time period\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.09 (0.15)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e[2.27\u0026ndash;4.09]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespiratory: Sender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.23 (0.06)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e[1.12\u0026ndash;1.44]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespiratory: Sender X Time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.15 (0.04)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e[0.78\u0026ndash;0.93]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespiratory: Sex node match\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.20 (0.07)\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e[0.70\u0026ndash;0.93]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespiratory: Absdiff\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.03 (0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e[0.89\u0026ndash;1.04]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEndogenous network dependencies\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep: Edges\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.19 (0.25)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e[0.16\u0026ndash;0.48]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep: Reciprocity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.51 (0.06)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e[1.48\u0026ndash;1.92]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep: GWESP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.29 (0.06)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e[1.19\u0026ndash;1.53]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep: GWODEG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.42 (0.23)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e[0.05\u0026ndash;0.14]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHead: Edges\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.37 (0.26)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e[0.15\u0026ndash;0.42]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHead: Reciprocity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.51 (0.06)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e[1.45\u0026ndash;1.91]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHead: GWESP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.29 (0.06)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e[1.18\u0026ndash;1.53]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHead: GWODEG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.50 (0.23)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e[0.05\u0026ndash;0.12]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGastro: Edges\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.76 (0.22)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e[0.10\u0026ndash;0.25]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGastro: Reciprocity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.51 (0.06)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e[1.45\u0026ndash;1.90]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGastro: GWESP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.30 (0.06)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e[1.19\u0026ndash;1.53]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGastro: GWODegree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.40 (0.24)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e[0.05\u0026ndash;0.13]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespiratory: Edges\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.74 (0.25)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e[0.03\u0026ndash;0.10]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespiratory: Reciprocity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.50 (0.06)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e[1.44\u0026ndash;1.90]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespiratory: GWESP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.31 (0.06)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e[1.21\u0026ndash;1.54]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespiratory: GWODEG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.36 (0.23)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e[0.06\u0026ndash;0.15]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eNote\u003c/em\u003e: \u003csup\u003e***\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0; \u003csup\u003e**\u003c/sup\u003e= \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; \u003csup\u003e*\u003c/sup\u003e = \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eMain effect of respiratory symptoms predicting being nominated as optimistic. The effect for Time 1 is presented in a black line and for Time 2 in a dark gray line. The light grey line represents the main effect irrespective of time. The shaded areas indicate 95% confidence intervals.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eEndogenous Network Dependencies\u003c/h2\u003e \u003cp\u003eThe significant reciprocity term indicated that optimism nominations were more mutual than expected by chance in all variables. The significant GWESP effect implied that individuals who nominated others as optimistic, their nominated peers also perceived others as optimistic, showing a tendency to create triads. The GWODEG term indicated that some people had generally lower thresholds of perceiving others as optimists.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eGoodness-of-Fit Assessment\u003c/h2\u003e \u003cp\u003eTo examine the quality of TERGM fit, we conducted a test to simulate one hundred new networks based on the model parameters and covariates and compared those with the networks observed from the data of the present study. The distributions of the new networks matched the observed distributions of the same statistics well enough suggesting that the estimated TERGMs of the study are well fitted to the data.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study aimed to uncover the extent to which physical health symptoms predicted perceptions of optimism in freshmen using the SNA approach with a multilevel design. By combining self-reported and peer-reported data, our research provides method-independent insights into how physical health symptoms intertwines with optimism within a social context, specifically within university classrooms. Notably, sleep disturbances and respiratory infections were significant predictors of optimism perceptions, with individuals experiencing these health issues being less likely to be seen as optimistic by their peers. Furthermore, the impact of respiratory infections on optimism perceptions persisted over time, highlighting the long-term social implications of physical health on interpersonal judgments.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eIncoming Relationships: Which Physical Health Symptoms Predict Being Perceived as Optimistic?\u003c/h2\u003e \u003cp\u003eThe models examining the relationship between physical health symptoms and perceptions of optimism revealed compelling findings. Individuals experiencing sleep disturbances and respiratory infections were consistently perceived as less optimistic by their peers. The association between these two health conditions and optimism has been more extensively documented in empirical research than headaches and gastrointestinal problems (Haack \u0026amp; Mullington, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Koo et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Lemola et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Prior studies have shown that sleep disturbances are linked to a more pessimistic outlook (Hernandez et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Our findings align with this evidence, further suggesting that individuals suffering from sleep disturbances may not only experience a pessimistic view themselves but also be perceived by others as less optimistic. Sleep disruptions can significantly impact emotional well-being by increasing stress, fatigue, and discomfort, all of which contribute to a diminished optimistic outlook (Lau et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Similarly, respiratory infections have been previously associated with a less optimistic outlook (Koo et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Consistent with these findings, our results extend this understanding by suggesting that not only do individuals experiencing respiratory infections exhibit lower optimism, but they are also perceived by others as less optimistic. This may be due to the visible physical symptoms of illness, such as fatigue, congestion, and discomfort, which could lead observers to associate these individuals with lower levels of positivity. Given that social perception is influenced by nonverbal cues and overall well-being, the sustained effect of respiratory infections on optimism perception over time suggests a deeper connection between physical health and social judgments of optimism.\u003c/p\u003e \u003cp\u003eOur study highlights the formative role of social connections in shaping perceptions of optimism. Previous research has established a link between self-reported health issues and a more pessimistic outlook, and our findings suggest that this relationship may extend to peer-reported perceptions as well. Social relationships serve as channels for transmitting attitudes, emotions, and even health-related information (Christakis \u0026amp; Fowler, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Fowler \u0026amp; Christakis, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), which can, in turn, shape how individuals are perceived within their social networks. These findings feature the possibility that one\u0026rsquo;s physical health not only influences personal emotional states but also affects how others perceive and attribute subjective characteristics, such as optimism. Observable health symptoms may lead to implicit biases in social interactions, influencing judgments about an individual\u0026rsquo;s disposition and potentially altering social dynamics over time. This suggests that social perceptions of optimism are not formed in isolation but are shaped by both individual health status and broader interpersonal influences.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eOutgoing Relationships: Which Physical Health Symptoms Predict Perceiving Others as Optimistic?\u003c/h2\u003e \u003cp\u003eOur models revealed a noteworthy pattern in the relationship between physical health symptoms and perceptions of optimism. At Time 1, individuals experiencing sleep disturbances and respiratory infections were less likely to perceive their peers as optimistic. However, this trend shifted at Time 2, where individuals with respiratory infections were more likely to perceive others as optimistic. A plausible explanation for this reversal lies in the evolving dynamics of social interactions. During the initial stages of social integration, individuals experiencing physical health symptoms may have had limited social engagement, leading to fewer opportunities to form positive perceptions of their peers. Physical discomfort, fatigue, or social withdrawal associated with these symptoms could have contributed to an initial pessimistic bias. However, as time progressed and these individuals became more socially integrated, their increased exposure to and engagement with peers may have provided them with richer social information, enabling them to develop more positive perceptions of others (Cohen, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). This shift emphasizes the importance of considering the temporal and contextual aspects of social perception, particularly in relation to physical health. Future research should further explore the underlying mechanisms driving this change, such as increased familiarity, changes in emotional states, or the role of supportive social interactions in shaping optimism perceptions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and limitations\u003c/h2\u003e \u003cp\u003eAmong the strengths of this study are its sample size, longitudinal design, perspective for integrating actor and partner reports, and statistical approach. However, the interpretations are limited to undergraduates from a Western, educated, industrialized, and democratic nation. Studies featuring samples with diverse demographic and cultural characteristics are necessary to ensure the robustness of these findings (Henrich et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The naturalistic setting adopted did not allow for explicit control over how, when, and how frequently the participants interacted during the investigation, which might have shed light about important conditions under which this influence occurred. Future studies could offer valuable insights into this topic by controlling for these variables. Ultimately, future studies could incorporate additional measurement time points and reduce the intervals between measurements to track changes in zero-acquaintance relationships as time progresses (Beer, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study offers valuable insights into how physical health symptoms shape social perceptions of optimism within university classrooms. Our findings show that sleep disturbances and respiratory infections led individuals to be perceived as less optimistic and to perceive others similarly at Time 1. At Time 2, individuals with respiratory infections continued to be seen as less optimistic, while their own perceptions of others shifted toward greater optimism. This suggests that social exposure and integration over time may either maintain or alter perceptions of optimism. Our results expand existing literature by emphasizing the role of social judgments within peer networks, going beyond self-reported experiences of optimism. Given the link between optimism and qualities such as social desirability, leadership, and emotional resilience, biased perceptions of individuals with health symptoms could lead to social exclusion and reduced support. This, in turn, may negatively affect emotional well-being and hinder social integration, particularly in university settings where peer relationships play a key role. The study highlights the importance of social support in health outcomes, as biased perceptions can limit access to emotional and practical support. To improve perceptions and outcomes for individuals facing physical health challenges, addressing these biases through educational initiatives, social interventions, and healthcare policies is necessary.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Standards Committee of the Faculty of Education, Psychology and Social Work, University of Lleida and is in accordance with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. All participants were informed about the research and gave explicit consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data and code associated with the study are available at Open Science Framework: https://osf.io/82rk4/?view_only=c5d09d74f36f4e41a4bb2a154cdbc5b6\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there is no conflict of interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no funding\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCTG: Conceptualization, Methodology, Data curation, Writing - Original draft preparation. 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Group Dynamics: Theory Res Pract. 2015;19(1):45\u0026ndash;61. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1037/GDN0000021\u003c/span\u003e\u003cspan address=\"10.1037/GDN0000021\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Optimism, physical health, social context, social network analysis","lastPublishedDoi":"10.21203/rs.3.rs-6310226/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6310226/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground: Extensive literature has explored the association between optimism and enhanced physical health. Despite this well-established link, understanding specific mechanisms by which physical health can influence the perception of optimistic peers remains a challenge. The biosocial perspective highlights the role of biological differences in the development of positive psychological attributes, such as optimism, while also recognizing the significant influence of the social environment on this relationship. In this study, we aim to explore the extent to which physical health symptoms predict the perception of being perceived as an optimistic individual.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMethod: A cohort of 240 university students across 14 undergraduate classes participated in this longitudinal study, completing two assessments spaced seven months apart. Participants completed (1) self-report measures on physical health symptoms, encompassing sleep disturbances, headaches, respiratory infections, and gastrointestinal problems, and (2) pencil-and-paper tasks to identify classmates whom they perceived as optimistic individuals. Employing a longitudinal social network analysis approach, we utilized the temporal exponential-family random graph model (TERGM) in Rstudio to analyze the influence of self-reported physical health symptoms on the selection of optimistic peers.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResults: Our findings revealed that symptoms associated with sleep disturbances and respiratory infections predicted less nominations of being perceived as an optimistic individual. Notably, the negative association with respiratory problems persisted over time.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConclusions: The presence of specific physical health issues can significantly impact how individuals are perceived by others, concerning optimism. These results expand existing literature by emphasizing the role of social judgments within peer networks.\u003c/p\u003e","manuscriptTitle":"How Does Physical Health Influence the Perception of Optimism? A Longitudinal Social Network Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-16 09:32:02","doi":"10.21203/rs.3.rs-6310226/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-06-23T08:08:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"282864075336574031950823976788933353078","date":"2025-06-13T06:35:31+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-11T09:34:52+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-14T11:35:43+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-03-28T11:39:10+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-28T10:06:47+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2025-03-28T10:05:41+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0d52a4f8-1ffe-43a2-a297-45976290315f","owner":[],"postedDate":"June 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-06-16T09:32:02+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-16 09:32:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6310226","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6310226","identity":"rs-6310226","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}