Objectives: Social isolation and loneliness are serious public health issues given the association with negative physical, mental; and cognitive health outcomes and increased risk for mortality. Due to changes in life circumstances many aging adults are socially isolated and experience loneliness. We examined the relationships among four correlated but distinct constructs: social network size, social support, social isolation, and loneliness as they relate to indices of health and wellbeing among diverse subpopulations of older adults. Guided by WHO’s International Classification of Functioning, Disability and Health (ICF) we also examined factors that predict loneliness and social isolation.Methods: Analyses of baseline data from sample of older adults who participated in an intervention trial that examined the beneficial effects of a software system designed to support access to resources and information, and social connectivity. Participants included 300 individuals aged 65–98, who lived alone, were primarily of lower socio-economic status and ethnically diverse. Participants completed a demographics questionnaire, self-report measures of health, depression, social network size, social support, and loneliness.Results: Loneliness was strongly associated with depression and self-ratings of health. In turn, greater social isolation and less social support were associated with greater loneliness. Social isolation was associated with depression and lower self-ratings of health. The association between social isolation and health was mediated by loneliness. Individuals in the older cohorts (80+) reported less social support. With respect to loneliness, having a smaller social network, more functional limitations, and limitations in engaging meaningful activities was associated with higher levels of loneliness and greater social isolation.Conclusion: The findings underscore the importance of social connectively to wellbeing for older adults and suggest that those in the older cohorts, who have a small social network, and with greater physical and functional impairments may be particularly vulnerable to being socially isolated and lonely. The findings provide guidance for future interventions. In this regard, we discuss how Information and Communication Technologies (ICTs) may be used to promote social connectivity and engagement. Strategies to make the usability and availability of these applications for aging adults are highlighted. The number of people in the United States aged 65+ will increase to about 98 million by 2060, with the fast-growing cohort of the “oldest old” (85+) projected to number 14.6 million by 2040 . The burRecently, increased attention has been directed toward social isolation and loneliness as significant health risks for aging adults. Changes in life circumstances, such as retirement, loss of partners or friends, financial circumstances, health declines, and mobility challenges make older people vulnerable to becoming isolated and lonely. Current estimates suggest that approximately, one-quarter of community dwelling adults aged 65 and older are socially isolated  and thatSubstantive results in the literature link social isolation and loneliness to heightened risk for physical difficulties, mental health problems, cognitive deficits, functional declines, and mortality e.g., . Data frCurrent models of “successful aging” e.g., , 2001 poWith the increasing number of adults in the older cohorts and other demographic trends, such as geographical dispersion of families and changes in family structures, social isolation will continue to be an issue in the foreseeable future. This is especially true in light of the COVID-19 pandemic where stay-at-home requirements curtailed opportunities for face-to-face interactions, participation in social activities, and access to social networks and support. Much is being written about the potential implications of the enforced social restrictions on mental health and well-being e.g., . As noteSocial isolation and loneliness are distinct constructs, which are related but only moderately correlated. Social isolation can be measured objectively and refers to social network size and the existence and interconnections among different social ties. Loneliness is subjective and refers to a person’s self-perceived lack of social support and companionship. Social support refers to the provision of emotional, instrumental, or informational resources to help an individual cope with stress and life events  and is rIn this study, we had the unique opportunity to examine the relationships among aspects of social engagement and the relationships of these factors to health outcomes among a large and diverse sample of older adults who live alone in the community. Although living alone has been associated with higher rates of isolation and loneliness, the relationship between living alone and these factors is complex. As noted by The sample for the analyses was comprised of participants in the Personal Reminder, Information, and Social Management (PRISM) randomized field trial , 2018, wSeniorHealth.Gov); an annotated resource guide; a dynamic classroom feature; a calendar; a photo feature; email; games; and online help. Participants also completed 6- and 12-month follow-up assessments administered by assessors blinded to treatment condition. The Institutional Review Boards at the sites approved the study and all participants provided informed written consent. Here we report on data from the baseline assessment from all study participants.Potential participants contacted the site study coordinator and completed a telephone screening that assessed eligibility. For those eligible, a home baseline assessment was scheduled. During this assessment participants provided informed consent. An assessor trained and certified in the study protocol administered the assessment. Participants were compensated $25 for the assessment. Participants were then randomly assigned to the PRISM condition or a Binder Control condition. Those in the PRISM condition received hardware and software training and had the PRISM system installed their home for 12 months. Those in the Binder Control condition received a binder containing content that paralleled the PRISM system in a non-electronic form . PRISM included Internet access ; 33% of the sample was ≥80 years and 15% were 85+ years. Participants were ethnically diverse (46% non-White), 89% had annual household incomes <$30,000, and 39% had high school or less education , Miami (FL), and Tallahassee (FL) regions of the United States]. The sample was primarily female (78%) and ranged in age from 65 to 98 years , comparative fit index (CFI), Tucker-Lewis index (TLI), and the standardized root mean residual  or on a set of parameters.We then conducted a multiple group analysis of the structural equation model, which involved testing the efficacy of adding increasing levels of equality constraints on the parameters for the younger-older adults and the older-old adults. We started with a model where the groups were allowed to differ on most parameters. Equality constraints were then added to various families of parameters. This allowed us to sequentially test if the means of the variables, the factors loadings, the paths, and the residual errors of the groups differed. The change in Xn = 101) and the younger-old (n = 199) samples as well as univariate contrasts are provided in n = 299), health (n = 298), social isolation (n = 299), and social network size (n = 299).2(8) = 11.00, p = 0.20, RMSEA = 0.04 90% CI , CFI = 1.00, TLI = 0.99, and SRMR = 0.02 for the model without age analyzed as a grouping variable. As shown in the z = −3.16, p = 0.002) on depressive symptoms, meaning greater social isolation predicted more depressive symptoms. The indirect effect of social isolation on depressive symptoms through loneliness was significant . The path of social isolation to depressive symptoms through social support and then loneliness was also significant .We tested the mediation effects of social network size and social isolation to depressive symptoms through social support and loneliness. There was a significant direct effect of social isolation  on depressive symptoms was not significant. However, the indirect effect of social network size on depressive symptoms mediated through social support and then loneliness was statistically significant . Social support had a statistically significant indirect effect on depressive symptoms  through loneliness.The total indirect effect of social network size  = 68.97, p < 0.01, RMSEA = 0.07, 90% CI , CFI = 0.97, TLI = 0.97, and SRMR = 0.10. Those in older age group had lower social support . In summary, the measurement invariance analysis showed that a model constraining the two groups to be equal, with the exception of the latent variable of social support, had the best model fit.We replicated the previous structural equation model for the two sub-groups of older adults (65–79 and 80–98). 2(8) = 11.46, p = 0.18, RMSEA = 0.04 90% CI , CFI = 1.00, TLI = 0.99, and SRMR = 0.01 . However, there was a significant total indirect effect of social isolation  on ratings of health through two paths: (1) through loneliness , and (2) through social support and then through loneliness . The indirect effect of social isolation to ratings of health through social support was not significant  nor was the total indirect effect of social network size to social isolation . However, the indirect effect of social network size to self-ratings of health was mediated through social support and then loneliness . Finally, social support had a significant positive indirect effect on health through loneliness .Social isolation was not directly related to ratings of health  = 63.86, p = 0.02, RMSEA = 0.06, 90% CI , CFI = 0.97, TLI = 0.97, and SRMR = 0.10. As observed for depression, the older group had lower scores on the latent social support variable .We replicated the previous analysis with the two subgroups of older adults (aged 65–79 and 80–98). 2(20) = 25.91, p = 0.17, RMSEA = 0.03 90% CI , CFI = 0.99, TLI = 0.99, and SRMR = 0.03]. The interrelationships of social support, social isolation, social network size, and loneliness were identical to those found for depression. Cognition was not significantly predicted by any of the social variables and there were no significant indirect effects.The model tested for cognition was the same as for depressive symptoms and health . Model fM = −0.47, z = 2.59, p = 0.01). The best fitting model indicated the paths to depressive symptoms to be significantly different between the two groups. This model showed good fit  (see 2(1) = 3.98, p = 0.03], and social isolation  were significantly different between the two age groups if tested separately or together . However, the parameter estimates for the individual group paths for social isolation and cognition were not significant for the younger group, . For social network size the effect was significant for the older group , but not significant for the younger group . For the older group a having a larger social network was related to higher cognition.We replicated the previous analysis with the two subgroups of older adults (aged 65–79 and 80–98). As loneliness and social isolation were pivotal variables in our model, we decided to more thoroughly test the factors that predicted both constructs. Guided by the WHO’s international Classification of Function, Disability, and Health ICF;  model, wF = 20.23, p < 0.001, R2 = 0.46. The step entering personal factors explained the least variance and was not statistically significant , and thus we do not discuss individual parameters  = 19.18, p < 0.001, ΔR2 = 0.13]. The only significant variable was functional limitations; having more limitations was associated with higher degrees of loneliness . In the third step, we entered the environmental variables and this step also explained a great deal of variance F = 44.02, p < 0.001, ΔR2 = 0.30. Having a larger social network was associated with less loneliness , and higher reporting of engaging in valued activities was also associated with less loneliness .The full model for loneliness was strongly significant ters see . In the F = 12.53, p < 0.001, R2 = 0.34]. The step entering personal factors explained the least variance and was not statistically significant , thus we do not discuss individual parameters  = 8.91, p < 0.001, ΔR2 = 0.07]. Having more functional limitations with more limitations was associated with greater social isolation . In the third step, we entered the environmental variables and this step also explained a great deal of variance  in social isolation. Again, having a large social network was associated with less loneliness , as did higher reporting of engaging in valued activities .The full model for social isolation was also strongly significant [ters see . In the The aging of the population generates a pressing need to develop strategies to ensure that current and future cohorts of older people are able to live as independently as possible and enjoy a good quality of life. Recently, increased attention is being directed toward social isolation and loneliness among older people, as being isolated and lonely has a deleterious impact on physical, emotional, and cognitive health. Current data indicate that a large proportion of the aging population is socially isolated and lonely e.g., . StrategIn this study we had the unique opportunity to examine the relationships among a number of constructs related to loneliness and social isolation among a diverse sample of older adults living alone in the community. We explored the relationships between loneliness and isolation on depressive symptoms, health, and cognitive outcomes. Our sample included both younger-old and older-old individuals thus we could explore if these relationships varied according to these subgroups of older people. Our findings help to clarify the relationships among the various aspects of social support, isolation, and loneliness, as well as the resultant impacts on both mental health, physical health, and cognition. Further, we evaluated personal, environmental, and health factors that are associated with isolation and loneliness.Consistent with models of successful aging e.g., , 2001, oOur results showed that that loneliness was significantly related to symptoms of depression. There was also a significant direct path between social isolation and depressive symptoms. These findings suggest that isolation and loneliness are related but distinct constructs. Not surprisingly, social isolation was predicted by social network size. Social network size and social isolation were also related to social support, which in turn was related to loneliness. In general, older adults with larger social networks were less likely to be isolated and had greater perceived social support. They were also less likely to be lonely. Our findings showed that among the older adults, social network size was also related to cognition such that people in the older cohort with larger social networks scored higher on the composite measure of cognition. As noted, having a larger social network likely provided more opportunities for engagement and support. Alternatively, maintaining a social network may require a certain amount of cognition and individuals with higher levels of cognitive function may be better able to maintain those relationships. Others have found that size of one’s social network is also related to access to resources e.g., . The majWith respect to self-ratings of health, we found slightly different relationships. Specifically, loneliness had a direct negative impact on health but the relationship between social isolation and ratings of health was mediated by loneliness and social support. Although our study was limited to a subjective rating of health, others i.e.,  have fouWe did not find differences in the relationships among the variables for depressive symptoms or ratings of health between the younger-old and older-old adults. However, we did find that perceived social support was lower among the older-old people in our sample. This is important given the increasing number of people in this cohort and the relationship between social support and loneliness. People in the older age cohorts, especially older women are more likely to live alone and have fewer sources of support available due to changes in life circumstances.The findings from our regression analyses also point to the associations among individual and environmental variables and social isolation and loneliness. We found, not surprisingly that individuals with more functional limitations and those who less engaged in rewarding activities reported higher levels of loneliness. It is likely that functional limitations result in logistic hindrances to activity engagement.Our findings have important implications for the design of interventions. Strategies to increase the social networks older adults, enhance social support, and the ability to engage in meaningful and enjoying activities would likely be beneficial in terms of improving health outcomes, especially for those with functional limitations. These interventions might include creating affordable programs for older adults and connecting individuals to these programs or venues for peer support. It might also involve facilitating access to transportation services and enhancing community safety.Information and communication technologies offer vast potential in terms of promoting social engagement . For exaAccess to the Internet and email can also foster social connectivity. Findings from the PRISM trial  found thVirtual reality (VR) applications are increasingly being targeted toward older adults and provide a mechanism for interacting with others in an individual or group format as well as engaging in valuable activities. For example, one can engage in virtual travel or cultural events using a VR system. As noted, our data indicate that lack of engagement in valued activities is related to both social isolation and loneliness. These applications may also be especially beneficial for those with functional limitations who may have mobility restrictions. Design recommendations for VR systems targeted to older adults are being developed e.g., . DevelopHowever, although technology holds great potential in terms of fostering social interactions and decreasing loneliness among older adults it is important that technology applications are designed using a user-centered design approach where diverse and representative samples of older adult users are involved in the design process. This approach helps to ensure that the needs, preferences, and characteristics of aging adults are incorporated into the design of the technology application. Aging adults must be aware of technology developments and how various technology applications may improve their well-being and quality of life. Technology must be affordable and instructional and technical support must be available. Finally, technology cannot replace human contact, it provides a complementary vehicle for social interaction.Importantly, simply increasing social networks and social engagement is not sufficient for decreasing loneliness. The networks and engagement must be satisfying and result in enhanced feelings of support. Activities must also be rewarding and engaging. Finally, it is important to recognize that there is no one-size-fits-all approach to addressing loneliness or social isolation, and tailor interventions should be tailored to the needs, preferences, and contexts of individuals.Limitations of this study include the use of a self-report single item measure of health. Although this measure is commonly used and has been found to be related to objective health metrics. In addition, the data were cross-sectional and from a single time point which reduces the ability to make causal inferences. Our sample was largely of lower socio-economic status and restricted to individuals who lived alone in the community. Further, our sample was a convenience sample, that agreed to participate in a research trial. Thus, the findings may not generalize to other subpopulations of older adults. Finally, although path models are useful in conceptualizing interrelationships among variables of interest, these models only present associations and do not prove causal relationships. Despite these limitations the present study adds to the growing body of literature examining the important role of social engagement in promoting health and well-being among older people. It clearly demonstrated associations and pathways among social isolation, social support, and loneliness. We recognize of course that these relationships may be bi-directional or in the opposite direction hypothesize, for example that social isolation may be affected by health, which underscores the complexity of these relationships. Nonetheless, these data provide valuable guidance for the development of interventions to both prevent isolation and loneliness among those who are at risk and remediate these problems for those who are currently isolated and lonely. The findings also underscore the importance of directing attention to the public health risk of social isolation and loneliness especially in light of the COVID-19 pandemic.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by the Miller School of Medicine IRB. The patients/participants provided their written informed consent to participate in this study.SC and WR designed the study that is the source of the article. SC drafted the manuscript. JM designed the analysis, conducted and written in consultation with SC. All authors contributed to editing the article.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


It is widely documented that negative body image is a significant public health concern due to its association with symptoms of disordered eating and worse psychological well-being. The purpose of the study was to develop a path model of intrapersonal dimensions  as antecedents of perceived stress toward females’ body dissatisfaction and eating attitude disorders. A total of 300 females, including 100 aspiring fashion models, 100 athletes and 100 students (controls), between 15 and 24 years of age  participated in the study. Measures included level of psychological stress, self-efficacy and locus of control dimensions, body dissatisfaction and eating attitude disorder indices. A path analysis confirmed our research hypothesis. Comparing the three subsamples, we found better fit indexes in the two subgroups with elevated investment on their body image with respect the control group. More specifically, the model in the group of aspiring fashion models showed the best fit index. These results indicated that aspiring fashion models have a strong desire to maintain their low body mass or to become thinner. For this reason, a suitable involvement of expert health workers in the nutritional and psychological field could be extremely essential in the fashion world to maintain a healthier well-being. Body (image) dissatisfaction can be defined as the depreciation of body weight and physical appearance resulting from a perceived discrepancy between the actual body image and the ideal body image . SeveralAccording to Bandura’s social cognitive theory , self-efLocus of Control (LOC) is an important construct that has a significant impact on Bandura’s self-efficacy theories  and how The relationship between self-efficacy and locus of control is widely investigated ,15,16, cGenerally, stress can be a primary or secondary contributor to ill health via excessive and sustained sympathetic arousal or through associated behaviors, such as smoking, substance abuse and over- or inappropriate eating. Several studies suggested that stressful life events precede irregular eating and weight control behavior in young females, showing a close association between feelings of stress and body image problems ,27,28,29The medical term eating disorder describes an illness that is characterized by irregular eating behaviors and severe distress or concern about body weight or shape . NeverthIn the field of eating disorders, high-risk groups include people born after perinatal complications, individuals who suffered severe negative life events or people whose behavioral patterns will bring to the probable development of an eating disorder, such as excessive dieting, professional pressure to be thin and excessive exercise . AccordiIn the sport field, the majority of studies ,36,37 reTo our knowledge, the present study is the first to compare aspiring fashion models to a group of female athletes and a well-matched control group composed of females of the same age and cultural background. Based on these arguments, and given the limited research, the purpose of the study was to develop a path model of self-efficacy and internal locus of control variables as antecedents of perceived stress toward body dissatisfaction and eating attitude disorders. A path analysis was used to examine an integrative model involving intrapersonal dimensions (self-efficacy and locus of control) as predictors of females’ body dissatisfaction and eating disorder through the influence of the perceived stress. The hypothesized model is illustrated in age = 19.6, SD = 1.85). The analysis of variance (ANOVA) did not reveal any significant differences among the three groups in terms of age  = 1.24; p = 0.29; partial η2 = 0.08.In order to have a sample of participants as homogeneous as possible, participants were included if (a) they were aged between 15 and 24 years; (b) currently, they had to attend school; and (c) they could provide informed consent. Participants were excluded if did not meet aforementioned inclusion criteria. In this study, a total of 300 participants met all inclusion criteria: 100 aspiring fashion models, 100 athletes of different athletic disciplines and 100 female students (controls) between 15 and 24 years of age . Athletes were regional competitors of Track and Field . A control group was established in order to compare the study variables among young females who did not have a strong pressure to maintain a thin shape. Thus, students from two high schools and two college of the same age group and cultural background were included in this study. Researchers provided participants, parents/guardians and agents of aspiring fashion models with a full explanation of the goals and the protocol of the study.Each participant >18 signed a free written consent to participate. A consent form was signed by parents for the younger students and by agents for the aspiring fashion models. All of the procedures were conducted in accordance with the ethics stated in the Declaration of Helsinki, while the University Enna Kore Internal Review Board for psychological research (UKE-IRBPSY-05.21.01) gave ethical permission.First, the responsible researcher contacted the director of the “Look of the Year”. The procedures and objectives of the study were explicated in detail, and authorization for participation was obtained. Afterward, high-school and college principals and head coaches from the athletic clubs were contacted requesting permission to survey student athletes. When consent was received, researchers determined a day and time for them to complete the surveys.Measurements were executed in group format, with 15–20 participants per session (approximately 35 min.) in a quiet room under the supervision of two researchers. Upon completion of all tests, the anthropometric measurements (in light clothing and without shoes) were taken by a research assistant in another private room. Body weight and height were collected through portable digital scale and stadiometer. All participants performed a battery of tests, and confidentiality of their answers was assured. Aspiring fashion models were tested in a quiet room in the place of competition. The control group was tested in a room in either the university or the school at the end of lecture. Athletes were tested in a private location in the training facilities. During the assessment, no talking was permitted, and no feedback was provided.Body Dissatisfaction subscale of the Eating Disorder Inventory   was admiIt is a measure  of bulimIt is a 9-item, self-administrated questionnaire  measurinIt is a self-report measure  of disorIt is a 10-item psychometric scale  aimed toIt consists of 6 items . People 2 (m).Body Mass Index (BMI) was computed from values of weight and height using the standard formula of weight (kg)/heights) and the range. A one-way analysis of variance (ANOVA) was performed on the scores of dependent variables , comparing the means of each group. A Tukey’s post hoc test was used to describe the existent differences. Moreover, Pearson’s r coefficient was used to determine the relationship between the selected variables.The Kolmogorov–Smirnov test showed a normal distribution of the data. Data are expressed as means ± standard deviations(2), comparative fit index (CFI) and the root mean square error of approximation (RMSEA) and the standardized root mean square residual (SRMR). A significant χ2 value leads to the rejection of the null hypothesis that the model fits in the population. The CFI provides an evaluation of the difference between an independent model and the specified model. For the CFI, values over 0.90 propose acceptable fit, while values over 0.95 indicate a good fit [A path analysis was carried out to test the hypothesized model using Lisrel 8.80 . The googood fit . Accordigood fit , a RMSEAgood fit , in whicAll 300 participants completed the surveys. p = 0.001; models vs. athletes p = 0.001), perceived stress , eating attitude disorder , fatalism  and hetero-dependence (models vs. athletes p = 0.001), while scores on self-efficacy  and on internality  came out lower. Furthermore, post hoc comparisons showed significantly lower scores in the group of controls on body dissatisfaction (controls vs. athletes p = 0.001) and eating attitude disorder (controls vs. athletes p = 0.001), while scores on fatalism (controls vs. athletes p = 0.001) and hetero-dependence (controls vs. athletes p = 0.001) came out higher. Additionally, self-efficacy (athletes vs. controls p = 0.001), and internality (athletes vs. controls p = 0.001) were higher in the athletes, while perceived stress  came out lower.The one-way analysis of variance (ANOVA) results  were significant . SpecifiSkewness and kurtosis of the distribution of the total scores of the dimensions of the study were calculated: skewness values were between −0.55 and 1.14; kurtosis values were between −0.33 and 1.10.The relationships between the variables of the study were analyzed using Pearson’s r coefficient .As can be observed, body dissatisfaction was significantly and positively related to eating attitude disorder and perceived stress and negatively associated with self-efficacy. We found very strong relationships between the intrapersonal dimensions of self-efficacy and internal locus of control, which we assumed as the antecedents in our research hypotheses. Moreover, scores for perceived stress were significantly and positively related to eating attitude disorder and negatively associated with self-efficacy. Skewness and kurtosis of the distribution of the total scores of the dimensions of the study were calculated: skewness values were between −0.55 and 1.24; kurtosis values were between −1.17 and 1.10.2(5) = 9.23, p = 0.10; CFI = 0.98; RMSEA = 0.05; SRMR = 0.05) that suggest an excellent fit between model and data. The Model 1 is presented in We tested our hypotheses using a path analysis. First, we tested the model on the whole sample (N = 300), obtaining the following fit indexes , athletes (Model 3) and control group (Model 4), obtaining the fit indexes reported in As shown in To the best of our knowledge, the present study was the first to develop a path model of self-efficacy and internal locus of control variables as antecedents of perceived stress toward body dissatisfaction and eating attitude disorders, comparing fashion models to a group of athletes and a well-matched control group composed of females of the same age and cultural background. The results emphasized the importance of these dimensions in the context of body image. The research hypotheses were confirmed: in our model, we found substantial differences in the comparison of three subsamples. Although body dissatisfaction is a strong predictor of disordered eating, it is firstly important to underline that all participants in our sample did not concomitantly report severe levels of eating disorder symptomatology, as far as full syndrome anorexia or bulimia nervosa is concerned.First of all, post hoc comparisons showed that the aspiring fashion models had significantly higher body dissatisfaction index, perceived stress, and risk of eating attitude disorder, and lower self-efficacy and internal locus of control than athletes and controls; moreover, they reported a BMI below 18, the cut-off for under-nutrition. Similarly, previous studies ,42,43,55Pearson’s r correlation revealed very strong and positive relationships between body dissatisfaction, eating attitude disorder and perceived stress and negative relationship with self-efficacy. On the other hand, we found very strong relationships between the intrapersonal dimensions of self-efficacy and internal locus of control, which we assumed as the antecedents in our research hypotheses. Moreover, scores for perceived stress were significantly and positively related to eating attitude disorder and negatively associated with self-efficacy. According to the aforementioned literature, self-efficacy and internal locus of control, as intrapersonal dimensions to cope with environmental demands, represent some of the most important resource factors in stress appraisal processes. Because of these considerations, the impact of the stressful events has less negative effect on the health of the individual.In the last investigation of the present study, comparing the tested path model in the three subsamples , we obtained very good fit indexes; more specifically, the model of aspiring fashion models showed the better fit index than in the other two groups. In many sports, according to Hausenblas and McNally , athleteIn addition, as suggested in our results, aspiring fashion models can be much more exposed to the dangers of diet and exasperated thinness because of the few experiences, insecurity of future and probable competitive frustration that can diminish their ability to be resilient to stress. In a beauty contest, the competitive stress can cause candidates to intensify attempts to preserve their body weight below normal parameters. This attitude sometimes can bring conditions of food disorder. For this reason, the presence of skilled health workers in the field of nutrition and psychology can be extremely important in the field of fashion in relation to the training of girl models, where competitiveness and stress are unavoidable conditions.Notwithstanding the promising results, limitations of the study should be addressed. First, by using self-report inventories exclusively, data may be susceptible to selective or erroneous reporting. Second, the cross-sectional nature of the study does not permit us to state clearly the causal relationships between the variables.Future research should establish whether the higher risk of eating disorders among aspiring fashion models observed in this study is an effect of the demand of the profession to maintain a thin figure or if the fashion model profession is specially chosen by girls already predisposed to eating disorders.In conclusion, it is possible that health information on the psychological risks of eating disorders today has partially changed the attitudes of fashion models or, generally, of young females; however, they still remain at risk. For this reason, it is crucial to steer them away from unhealthy diet and potentially harmful weight-loss behaviors, providing the support of nutritionists and psychologists for the adoption of health eating and physical activity behaviors. It is also important to engage in these interventions, teachers, coaches and family members, as well as people working in the fashion industries.


Q and I2 statistic. We utilized the random-effect model to calculate the overall RR with 95% CI.  We systematically searched articles on electronic databases such as PubMed, Embase, Scopus, and Google Scholar between January 1, 2000 and July 30, 2020. Articles were independently evaluated by two authors. We included observational studies (case-control and cohort) and calculated the risk ratios (RRs) for associated with anemia and PD. Heterogeneity among the studies was assessed using the N = 4, RRadjusted = 1.17 . However, heterogeneity among the studies was significant . The pooled relative risk of PD in female patients with anemia was higher  as compared to male patients with anemia .  A total of 342 articles were identified in the initial searches, and 7 full-text articles were evaluated for eligibility. Three articles were further excluded for prespecified reasons including insufficient data and duplications, and 4 articles were included in our systematic review and meta-analysis. A random effect model meta-analysis of all 4 studies showed no increased risk of PD in patients with anemia ( This is the first meta-analysis that shows that anemia is associated with higher risk of PD when compared with patients without anemia. However, more studies are warranted to evaluate the risk of PD among patients with anemia. Parkinson disease (PD) is the second most common age-related neurodegenerative disorder, with more than 60,000 newly diagnosed cases yearly in the USA . The incThe common risk factors of developing PD are exposure to pesticides, genetic variants, environment toxins, idiopathic REM sleep behavior disorder (RBD), and focal cerebrovascular damage –6. HowevAnemia hampers erythropoiesis and enhances eryptosis, whereas insufficient iron is reported in the substantia nigra of PD patients , 17. IncGoal: The aim of this current systematic review and meta-analysis is to evaluate the published epidemiological studies for clarifying the association between anemia and risk of PD.Study the magnitude of the risk of PD in patients with anemia and without anemiaCalculate the magnitude difference of the risk of PD in male and female patients with anemiaReduce the confounding factors by evaluating the risk based on various adjustmentsWe did a systematic search on electronic databases such as PubMed, Embase, Scopus, and Web of Science between January 1, 2000 and July 30, 2020. The following search terms were used to collect relevant articles: “anemia,” OR “low level hemoglobin,” “iron deficiency” And “Parkinson disease.” The initial search was conducted by one author who is an expert in systematic review. Moreover, we searched in the reference lists of retrieved articles to ensure the comprehensiveness.The eligibility criteria were restricted to observational studies (case-control and cohort) and clinical trials  that evaluated the association between anemia and PD as a primary outcome. Studies were included if they were published in the form of (a) original study, (b) participants at least 200, (c) published in English, (d) provided clear definition of anemia and PD, and (e) provide proper effect size to summarized pool risk.We excluded studies if they published in the form of review, short report, poster, editorial, case-report, and correspondence.Two authors (TNP and YCW) independently reviewed all the titles and abstracts of retrieved articles. They used prespecified selection criteria to select relevant articles to be included in this meta-analysis. Any disagreement in this stage was resolved by the difference by discussing with third author.Same two authors developed data collection form for extracting the required data from the selected studies. They checked the study duplication by comparing authors' names, publication year, and location of study. Two authors collected the information about effect size , adjusted factors, total number of participants, number of anemia and PD patients, inclusion and exclusion criteria of anemia and PD, age, percentage of male, duration of study period, and location.Q and I2 statistic. Finally, publication bias evaluated by 3 funnel plot–based methods: the Egger's test, the Begg's test, and the trim and fill method.The Newcastle-Ottawa Scale (NOS) was used to assess the quality of each study. They calculated the NOS score and divided into two groups: low and high qualities. The heterogeneity was calculated among study-specific RRs using the Q statistic and quantified by the I2 value. The heterogeneity among the studies was categorized into four groups, namely, very low (<25%), low (25~50%), medium (50~75%), and high (>75%) [p value less than 0.5 is considered as a significant.The meta-analysis was performed on selected studies that evaluated the magnitude of the association by using adjusted ORs and HRs. The pooled risk ratio (RR) was calculated from selected studies to show the effect size. The random-effect model was used in this meta-analysis. We calculated statistical heterogeneity across the various studies which were tested using the Cochran h (>75%) –25. WhenInitial search in the electronic databases yielded 342 articles. A total of 335 articles were excluded after reviewing the titles and abstracts of retrieved articles. However, 7 articles went to full-text review, and 3 articles were further excluded due to not matching the prespecified selection criteria. Finally, 4 articles were included in the current systematic review and meta-analysis –29.  FigThis current systematic review and meta-analysis comprised of 4 studies including 3 cohorts and 1 case-control study . IncludeN = 4, RRadjusted = 1.17 . However, heterogeneity among the studies was significant   .N = 3, RRadjusted = 1.14  as compared to male patients with anemia   and female  patients with anemia.We pooled 3 studies in order to evaluate the risk of PD based on gender. The pooled relative risk of PD in female patients with anemia was higher ( = 0.51) . HoweverN = 3, RRadjusted with diabetes and hypertension = 1.10 , and heterogeneity was significant .We also evaluated the risk of PD in patients among the studies adjusted with most common confounding factors such as diabetes and hypertension. The pooled risk ratio was  all included studies quality were high and (b) consider adjusted effect size; therefore, risk of bias is low and (c) clear definition of anemia and PD risk.α-synuclein and etiology of PD are the main keys that can be used to explain the pathological mechanism of anemia and PD risk. Erythrocytes generate maximum portion of α-synuclein in the human blood [α-synuclein oligomer and total protein is higher in erythrocyte of PD patients when compared to normal patients [α-synuclein in PD has received wide attention over the past years. A previous mice study showed that microbial metabolites may increase the risk of neuroinflammation by expressing proinflammatory cytokines which ultimately lead to the development of motor symptom [The biological mechanism of PD and anemia is not fully understood yet. However, there are several biological explanations that can define their association. First, the neural degeneration and disease progression among the PD patients can be induced by either apoptosis or necrosis . The braan blood ; it has patients . Anxietypatients , 41. Deppatients . Moreove symptom . Other s symptom .PD is now one of the leading causes of disability and deaths globally. A significant amount of epidemiological studies have highlighted an increased prevalence of PD which raised sheer concern and urgent need for public health strategies –47. ApprOur first and rigorous meta-analysis has several strengths. First, this is the first meta-analysis that investigated the association between anemia and PD risk. Second, this study included four large observation studies from Taiwan, Israel, South Korea, and USA; healthcare data quality of these countries is world standard. They adjusted potential confounding factors to this study to reduce bias of effect size calculation. Third, this present study showed risk difference between male and female patients.However, this study has some limitations. First, number of included studies is only four, although all are high quality study design with larger number of participants. Second, we were not able to provide risk of PD based on severity of anemia  and BMI . Third, the pooled risk was not calculated based on location, study design, and other factors like smoking status, alcohol consumptions, and duration of anemia; it is because data were limited.Our study shows that the risk of PD was higher among the patients with anemia as compared without anemia. The risk of PD was higher in male patients. Therefore, more protective strategies should be taken in male patients when compared to female patients. The risk of PD among patients with anemia can be explained by some biological mechanisms like oxidative stress and downregulation of iron homeostasis. More observational studies in different regions and biological studies are warranted to clarify the mechanism underlying their association.


Background and Aim: Previous studies have suggested that the specific association between selenium (Se) and diabetes remains unclear. This study aimed to investigate the association between dietary Se and type-2 diabetes (T2D) in the Brazilian cohort [Cohort of Universities of Minas Gerais (CUME)].Methods and Results: This cross-sectional study was conducted with a large sample comprising 4,106 participants of the CUME project, a concurrent open cohort restricted to a highly educated population group, composed of graduates of federal institutions of higher education located in the State of Minas Gerais, Brazil. Data on socioeconomic and dietary characteristics, as well as anthropometric measures, were collected from each subject for analysis. The sample was classified into energy-adjusted tertiles of dietary Se intake (μg/day). Differences in the continuous data were evaluated by the Kruskal–Wallis H-test , and the χ2-test assessed differences in qualitative data. As there was no relationship between T2D and Se intake in the bivariate analysis, multivariate analysis was not performed. The prevalence of T2D in the studied population was 2.8%. The mean age was 36 years. Regarding gender, 1,209 are males and 2,807 are females. Among females, the mean Se intake was 165.12 μg/day and the mean intake was 157.4 μg/day. Among males, it was 168.4 μg/day. Significant differences were observed across all Se intake tertiles in terms of age, gender, activity level, alcohol intake, energy intake, sugar, carbohydrates, lipids, fiber, and energy-adjusted meat intake. However, no significant differences were observed across all Se intake tertiles in terms of BMI, smoking status, and T2D. The results indicated that there was no significant association between dietary Se intake and the prevalence of T2D.Conclusion: Dietary Se intake was not associated with the prevalence of T2D, despite the high intake of this micronutrient in the sample. These results contradict studies that identified the association between Se intake and T2D, with values of Se intake much lower than those observed in this study. Thus, this relationship seems to remain controversial. Diabetes is a metabolic disorder that results from an insulin-production deficiency or its action, having hyperglycemia as one of its main signs . AccordiOf the diabetes cases, about 90% corresponds to type-2 diabetes (T2D) mellitus . HyperglAntioxidant nutrients play an important role in the defense of our body by reducing the oxidative stress and preventing the emergence of chronic diseases, mainly by neutralizing free radicals and their metabolic effects. Vitamins A, C, and E and minerals, such as zinc, selenium (Se), copper, and manganese, which are present in our diet, are among the nutrients related to antioxidant status .via the sulfur assimilation pathway. Plants absorb these salts from soil and convert them into the organic form, selenomethionine and selenocysteine, which may be incorporated into proteins, originating from selenoproteins , a plant-based food, stands out as an exception, as it is the richest known source of selenium. The Brazil nut, which is part of the oilseed group, together with walnuts, peanuts, and cashew nuts, has a higher concentration of selenium. However, the concentration of selenium in Brazil nut varies considerably, depending on the geographic region of cultivation and the ability of the plant to absorb the mineral. Cardoso et al.  levels and glucose metabolism. Some observational studies have identified an association between high Se intake and a higher risk for T2D , 13, 14.Despite these findings, in the meta-analysis by Kohler et al. , the autHowever, Vincet et al.  have repIn this sense, epidemiological studies on Se intake by the Brazilian population have identified that the Se daily intake has been above the recommended daily intake (RDI). The Se RDI for adults is 55 μg/day with a tolerable upper intake level (UL) of 400 μg/day . The Se However, research with smaller samples and residents of a single municipality, such as São Paulo, Teresina, and Manaus –23, has Thus, this study aimed to investigate the association between dietary Se and T2D in a Brazilian cohort (CUME).This is a cross-sectional study with participants from the CUME project of two collection waves  in a virtual environment. In these two collection periods, 4,987 graduates answered the online survey questionnaire.Universidade Federal de Viçosa (UFV) and Universidade Federal de Minas Gerais (UFMG) .The CUME project is an open cohort with graduates from federal institutions of higher education in Minas Gerais, Brazil. The objective of the cohort is to assess the impact of specific food groups, nutrients, and dietary factors, and the nutritional transition on non-communicable diseases (NCD). The design, dissemination strategies employed, and project baseline profile have already been detailed in a previous publication . The CUMn = 531), from other nationalities (n = 22), Brazilians living abroad (n = 173), and likely type 1 diabetes: she/he was not diagnosed with diabetes in adulthood and uses insulin (n = 13). Participants with energy consumption below 500 kcal (n = 2) and above 6,000 kcal (n = 128) (n = 184), nuts intake >100 g/day (n = 91) were also excluded from the study. Thus, the study sample consisted of 4,016 adults who graduated from the referred institutions and answered the 2016 and 2018 baseline CUME questionnaires.In this study, we did not include participants with incomplete questionnaires regarding demographic data (n = 128) , meat inInvitations to participate in the research were sent by email to all graduates (graduates and postgraduates) from UFMG and UFV trained in the periods mentioned previously. The email addresses used were those in the Alumni Associations (UFV) databases and the Universities Technology and Information Directorates (UFMG).http://www.projetocume.com/questionario). The first part consisted of questions related to sociodemographic and economic characteristics, lifestyle, individual and family referred morbidity, medication use, personal history of clinical and biochemical exams from the last 2 years, and anthropometric data. The second part of the questionnaire was sent a week after completion of the first part and had a quantitative Food Frequency Questionnaire (FFQ) composed of 144 food items, based on an original version previously validated in Brazil , which was divided into two parts  intake” was based on self-reported data on food intake; and subsequently, Se intake (μg/day) was calculated. Daily Se intake was adjusted by caloric intake using the residual method  and analSociodemographic, lifestyle, and food intake were used as adjustment variables. Sociodemographic variables were gender  and age . Regarding lifestyle variables, smoking status , alcoholic beverage intake , and physical activity  were assessed.Food intake data were obtained from a quantitative FFQ. To minimize errors in the data collection process, images of food items and utensils were made available to facilitate the estimation of portion size and filling in of the report, and to obtain a more reliable response regarding the intake of the participants . There wConsumption frequency of each food was transformed into daily consumption. Subsequently, daily consumption (grams or milliliters) was calculated by multiplying the portion size by the frequency of consumption. For the calculation of caloric intake  and nutrients, Brazilian tables of the nutritional composition of foods were used  and, if For food consumption, the following were evaluated: energy intake, protein, meat, total lipids, animal fats, the relationship between saturated and polyunsaturated fatty acids, carbohydrates, sugars, fibers, and alcoholic drinks. For sugars, intake was quantified in grams of table sugar, brown sugar, honey, sweet treats, and soft drinks. Dietary variables were studied as a continuous variable and adjusted for energy using the residual method .2-test was performed to evaluate differences in qualitative data. Analysis of the prevalence of T2D according to the tertile of Se intake adjusted for energy and adjustment variables was carried out using the Kruskal–Wallis H-test (non-parametric data) χ2-test for categorized data. Hierarchical cluster analysis was carried out to group the Se intake using the centroid method to calculate Euclidean distances. All analyses were conducted using the SPSS statistical software, version 18.0, considering a significance level of 5%.Sample characteristics are expressed as absolute and relative frequencies, or median and interquartile interval (percentile 25 to percentile 75), according to the medical diagnosis of T2D and sociodemographic, anthropometric variables, lifestyle, and food intake. Differences between continuous variables and categorized according to the presence or absence of T2D were assessed by the Mann–Whitney  test, and the χn = 112) had T2D at the cohort baseline. The median energy-adjusted Se intake was 143.5 μg/day, and it did not differ with the intake of those with or without diabetes. The median, not energy-adjusted Se intake was 137 μg/day, and the average consumption was higher among men (148.9 μg/day) than among women (131.4 μg/day).Of the total participants, 2.8% (p < 0.05) between the participants with and without T2D (The food groups consumed by the participants that most contributed to selenium intake were meat and meat products (58.2%), starch (26.2%), dairy (8.2%), and oilseed, presented in the FFQ as Brazil nuts, walnuts, peanuts, and cashew nuts (7.5%). Age, gender, BMI, smoking cigarettes, and sugar intake differed statistically (hout T2D .p < 0.05). On the other hand, selenium intake was not associated with the presence of diabetes.The characteristics of the participants according to daily energy-adjusted Se intake, as well as sociodemographic, lifestyle, and dietary characteristics are shown in p < 0.05), but no significant association with T2D prevalence (p < 0.05) was seen.The distribution of the participants according to energy-adjusted Se intake and T2D prevalence is shown in We conducted a cross-sectional study on a sample of 4,016 young adults from a cohort of public University graduates in Brazil, with the primary objective of investigating the association between dietary Se and T2D. The prevalence of T2D in the studied population was 2.8%, which is close to that estimated in a study with a representative sample of Brazilian adults in 2019 for people with over 12 years of schooling (3.5%) .Although the study participants were still young, T2D was seen to be more prevalent among the elderly, confirming that the prevalence of T2D increases with advancing age. However, it is noteworthy that the effect of nutrients on health outcomes may require a long exposure time. Cohort studies are recommended for this evaluation. Thus, although the CUME project is a cohort study, this is a cross-sectional study. In the future, we will verify that a long time of exposure to high consumption of selenium may favor T2D. Furthermore, this study did not evaluate blood selenium biomarkers (such as plasma selenium and erythrocytes), which provide more reliable information on selenium intake and the body reserve of this mineral .It is also noteworthy that not all selenium ingested is absorbed. The Se absorption mechanism differs between consumed sources, and this difference determines its absorption rate. Most selenite is absorbed in the duodenum by passive diffusion, while selenate is actively absorbed in the ileum by cotransport with sodium ions. Selenium from selenomethionine is also absorbed in the small intestine, with the highest absorption rate in the duodenum. Its absorption occurs through the sodium-dependent system and has the same mechanism as the methionine, as well as selenocysteine, which competes with cysteine, lysine, and arginine . FurtherThe profile of the participants in this study was previously described by Gomes-Domingos et al. . It is ap < 0.05). However, these studies  to be evident. Nevertheless, some epidemiological research on the association between selenium and fT2D mentioned in this article also used an estimate of the selenium dietary intake assessed by means of a food record , 45, 49.Since Se has multiple effects on the human body, it may be both protective and a risk for T2D. According to Mueller et al.  and SteiThe main strength of this article lies in the fact that it is the first to assess the association between dietary Se intake and T2D in the Brazilian population. The studies mentioned here have been conducted in the United States, Europe, and China. It is important to assess this association, particularly for the Brazilian population, because of differences in ethnicity, geography, and eating habits, which may affect the results. It is important to know that Se is a very common nutrient in food, especially in Brazil nuts, known in the world as the food with the highest concentration of this nutrient . HoweverNevertheless, there are some limitations to this research. First, it is a cross-sectional study, which is not able to explain the causal relationship of an outcome; therefore, further prospective studies are needed to confirm the findings. Second, the information is self-reported in an online questionnaire, and the serum Se level has not been measured. Studying the relationship between dietary Se, serum Se, and diabetes may provide a more comprehensive understanding of this topic. On the other hand, the sample studied is extensive, including people from all Brazilian states, which allowed us to examine the association between dietary Se intake and diabetes, thus suggesting a reasonable representation of the Brazilian population in the study.This study did not identify an association between Se intake and T2D from the studied sample. Thus, this relationship is seen to remain controversial, and further research is required, especially of the cohort type. In addition to selenium intake, blood biomarkers should also be evaluated.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Universidade Federal de Minas Gerais (UFMG) (Protocol No. 596,741-0/2013. The patients/participants provided their written informed consent to participate in this study.LN and JD designed the study, interpreted the data, and wrote the manuscript. PC analyzed the data with supports by LN and AP. JB, HH, and AP built the database for the cohort. LN takes responsibility for the integrity of the data and accuracy of the data analysis. All authors have read the manuscript and took part in the discussion.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The RMSEA was 0.01, and the standardized RMR was 0.027. These results support the hypothesis that apathy, fatigue, depression and anxiety represent prevalent syndromes that can be separated in Parkinson's disease and that apathy is not just a subcomponent of depression or fatigue. The results of this study may contribute to a clearer diagnostic process for apathy, fatigue and depression and may aid in patient care.Apathy, fatigue and depression are amongst the most debilitating non-motor syndromes of Parkinson's disease (PD). The aim of this study was to examine the prevalence of apathy, depression, anxiety and fatigue and whether these syndromes are separable in PD. A total of 337 patients were examined using the Unified Parkinson's Disease Rating Scale (UPDRS part III), the Apathy Evaluation Scale, the Hospital Anxiety and Depression Scale and the Fatigue Severity Scale. Using standard cutoff criteria, the prevalence rates of significant apathy, mild-to-severe depression, mild-to-severe anxiety and severe fatigue were 23.7, 13.4, 15.4, and 17.8%, respectively. Next, confirmatory factor analysis was employed of items from these three clinical scales. A priori hypothesis testing including four different factors  was performed. The factor analysis revealed strong fit statistics for the model with χ Apathy is a multifaceted syndrome related to reduced goal directed behavior associated with emotional, cognitive, and behavioral components that may result in such diverse states as indifference, reduced or lack of concern, initiative, and decision making with far reaching consequences for daily living , 2. AwarSo far, success in treating apathy in PD is mainly a consequence of treating other neuropsychiatric symptoms such as improvement in apathy due to cholinesterase inhibitors as a secondary outcome to improvements in cognitive impairment or the treatment of associated depression . WithoutMore information on whether apathy is a subcomponent of fatigue and depression in PD and therefore a better separation of these symptoms would have significant implications for studying their underlying neural mechanisms in aetiopathogenesis as well as for differential diagnosis and treatment. Therefore, using confirmatory factor analysis (CFA) of individual items from three commonly used clinical rating scales, the aim of this study was to investigate the hypothesis that apathy, fatigue and depression are dissociable in PD. Because of the large conceptual overlap in these clinical scales, conducting CFA might provide insights into relevant item-clusters that are clearly separable from each other. Based on clinical (treatment) experience and theoretical considerations ,Data records from 337 patients  suffering from PD and recruited from the university hospital in Zurich were retrospectively analyzed. The inclusion criteria were age older than 18 years, diagnosed PD according to established criteria , completApathy was quantified using the German version of the Apathy Evaluation Scale  which cop < 0.05 level. Confirmatory factor analysis was conducted with the statistical software AMOS 25.0, using maximum likelihood estimation. Before performing statistical analyses, the dataset was screened for missing data and outliers. Before executing CFA, assumptions for conducting CFA were tested: Testing for multinormality  as well as testing for skewness and kurtosis) revealed a fairly normal distribution of indicators of latent factors which were consistent with the recommendations of Kline , root mean square residual (RMR), normed fit index (NFI), comparative fit index (CFI), incremental fit index (IFI), relative fit index (RFI), and the Tucker-Lewis Index (TLI). Standard quality recommendations were used to determine goodness of fit. These included a ratio of χ2 to degrees of freedom of 2:1 or less , values of RMSEA below 0.05 and non-significant, standardized RMR below 0.05, and NFI, CFI, IFI, RFI, TLI above 0.95 , complemented by inferential statistics including spearman correlational analysis and confirmatory factor analysis (CFA). Significance was accepted at a of Kline . Testingove 0.95 . To aid Demographics and characteristics for all subjects are displayed in First, the prevalence of apathy, depression and anxiety was assessed, using the following cutoff-scores: an AES score of 40 and above [relating to clinically significant apathy ] and a sp < 0.01), higher fatigue  and higher anxiety- and depression scores  (p < 0.05) and the total dose of dopamine agonists . There was a weak positive association between the AES and the total levodopa equivalent dose .Next, we conducted a spearman correlational analysis using the respective global or partial scores of the AES, HADS and FSS: a higher AES score (indicating greater apathy) was associated with higher motor impairment  . The AES2  = 511.5, p < 0.01 . The root mean square error of approximation (RMSEA) was 0.086 (p < 0.01), and the RMR was 0.0402. The overall fit in terms of the ratio of χ2 to degrees of freedom ratio was larger than a ratio of 2:1 (3.5:1). Modification indices as well as standardized residual covariances were examined to improve the fit of the model. This led to the removal of items yielding a further deconstruction  of the theoretical model and a subsequent 4-cluster model including the factors: (A) interest and motivation (B) perseverance, (C) positive mood, and (D) anxiety. Cluster (A) was composed of items A1_NewExperiences, A2_InterestedLearning, A3_Initiative, A4_Motivated, cluster (B) of items B1_TiredQuickly, B2_ProductivityAffected, B3_ProductivityInhibited, cluster (C) of items C1_CanLaugh, C2_Happy, C3_FutureIsBright, and cluster (D) was composed of items D1_Worrisome, D2_WorringGutFeeling and D3_Panic. Interestingly, modification indices suggested that there was unexplained variance among A1_NewExperiences and A2_InterestedLearning as well as among B1_TiredQuickly and B2_ProductivityAffected. Allowing the unexplained variance of these indicators to correlate, improved the model by 88.24 χ2 points. The final model yielded an overall fit of: χ2  = 58.9, p = 0.41, CMIN/DF = 1,034 . The RMSEA was 0.01 (p = 0.9), and the standardized RMR was 0.027. The validity was tested according to Gaskin et al. .One of the main obstacles for appropriately identifying and managing apathy in PD is the lack of gold standard criteria. Accordingly, there is still disagreement concerning the construct of apathy and its relation to depression and fatigue in clinical practice, particularly in disorders such as PD in which all of these neuropsychiatric syndromes may occur concomitantly. Without a distinct differentiation and diagnosis of the clusters of symptoms within one or another construct, the effective management of apathy, depression and fatigue remains hampered. Moreover, this lack of definitional clarity represents an impediment for the validation of measurement tools leading to measurement variation and variable prevalence estimations of apathy and related syndromes in PD.The presence of debilitating apathy negatively impacts on treatments for other interventions such as engaged participation in physiotherapy or taking advantage of the mobility gained by an improvement of motor symptoms due to optimal dopaminergic treatment or deep brain stimulation (DBS). Therefore, the assessment and accurate differential diagnosis of apathy and related syndromes remains crucial.With the aim of crystallizing a possible core of apathy and thus contributing to a clearer diagnostic process for apathy, this study examined whether apathy, depression, anxiety and fatigue can be separated in PD.With 23.7, 13.4, 15.4, and 17.8%, the prevalence rates of significant apathy, mild-to-severe depression and anxiety and severe fatigue as assessed by the AES, HADS and FSS are comparable to other clinical PD samples , 41—albeThe correlational analysis identified strong positive relationships between apathy, depression, anxiety, and fatigue besides confirming that in addition, PD apathy is associated with older age (weak association in the present sample) and lower dopamine agonist use (weak association), as has been shown before . None oThe large degree of theoretic ambiguity in the structure of these scales limits the interpretation of the correlational results. Therefore, we aimed at testing whether items of three widely used clinical scales  would cluster in four separate factors including (A) pure apathy (motivation and interest) (B) physical fatigue (C) loss of positive affect (pure anhedonia), and (D) anxiety. After removing variables with unexplained variance and consulting modification indices as well as standardized residual covariances, there were no items that clustered on single factors originating from different scales for the apathy cluster. Moreover, the model fit was highest when a hypothesized separate cluster including loss of interest was merged with the amotivation-factor.The final model demonstrating strong model fit characteristics was composed of the following final factors:A) (loss of) interest and motivationB) perseveranceC) (loss of) positive moodD) anxietyWhen recalculating correlations using these factors, the relationships remained significant, but the strengths of associations reduced . This may suggest a decrease of association due to removal of theoretic ambiguity with persisting fundamental neurophysiological interrelations based on the disruption of whole neurotransmitter systems.The identified factors are in line with previous studies proposing separate factors for negativity and anhedonia  and an AAfter the DSM-5 criteria, a Major Depressive Disorder (MDD) can be diagnosed when the individual is experiencing five or more of a total of nine symptoms during the same 2-week period and at least one of the symptoms should be either  depresseIn PD, the main differentiating clinical characteristic between depression and apathy is the mood component, as it remains “neutral” in the latter and negatively affected in the former . While aRegarding the interrelation of apathy and depression, we found a clear separation within the anhedonic spectrum: the (lack of) pleasure component clustered separately from the (loss of) interest component. This is in contrast to the results of Kirsch-Darrow et al. , who useIn general, our results indeed suggest that the motivation/interest factor  is devoid of a hedonic component, which has relevance not only for its theoretical conceptualization but also for research and therapy. It also argues against the hypothesis that apathy might be a subcomponent of depression. Based on these findings, amotivation and loss of interest could represent a precondition for lack of emotional responses. In analogy to the gate control theory of pain, amotivation/loss of interest as a symptom that is dissociable from reduced pleasure could be understood as upstream factor necessary for the generation of emotional responses. This view could help understanding the strong interrelation between apathy and anhedonia, as for example in depression. As has been cautioned , using lAnxiety can be defined as a future-oriented mood state associated with preparation for possible, upcoming negative events in the form of thoughts of future threat, muscle tension and avoidance . The curFatigue in PD can be subdivided into peripheral and central fatigue: whereas the first is composed of an increased loss of muscle strength after repeated contractions and can be objectively measured [sometimes also termed performance fatigability, see , motor fRegarding the interrelation between apathy and fatigue in the current sample, the results of the CFA suggest a separation between perseverance and motivation/interest, implying that in PD, physical fatigue can be separated from amotivation/loss of interest (pure apathy). This may relate to the fact that patients suffering from fatigue usually want to do activities but are limited due to lack of energy. Fatigue is also different from depression, as it is not related to mood . Furthermore, our results provide evidence for a separation between physical fatigue and depression. Not carefully distinguishing perseverance from MDD and PD could similarly lead to false positive diagnosis.Support for the separability of apathy, fatigue and depression in PD has broad theoretical and clinical implications. It suggests different neural mechanisms may underlie apathy, fatigue and depression. It implies that clinicians should screen for apathy, fatigue and depression to appropriately treat patients. Briefly, there are yet no specific treatment options for apathy or fatigue in Parkinson's disease. There is evidence that rasagiline and levodopa infusion therapy for fatigue and rivastigmine for apathy might be effective treatment options , 60, 61.To our knowledge, this is one of the first studies trying to separate apathy from fatigue, depression and anxiety using CFA in a large sample of PD patients. In accordance with previous studies, our data of 337 PD patients suggest that apathy, depression, anxiety and fatigue are prevalent syndromes in PD. However, comparing prevalence estimates between studies is often hampered as they show a substantial variation. This heterogeneity may be grounded on the current limited methodological possibilities which rely largely on self-report questionnaires of different scales applied to non-uniform PD patient cohorts and using different cut-off thresholds. Therefore, it is difficult to assess whether there is a diagnosis bias or if these constructs share common etiologic mechanisms. Our data suggest that apathy, depression, anxiety and fatigue are strongly interrelated making the development of a clearer theoretical and neural mechanistic separation a corner stone for future individualized treatment. In line with previous studies on the interrelation of apathy and fatigue, fatigued patients in the group of apathy represent the largest fraction in the current sample of comorbid patients. Using confirmatory factor analysis, apathy could be separated from depression, anxiety and fatigue: Pure apathy, defined as loss of motivation and interest, could be dissociated from physical fatigue, loss of positive affect (anhedonia) and anxiety. This argues against the hypothesis that apathy might be a subcomponent of depression. A clearer conceptual understanding of apathy, fatigue and depression in Parkinson's disease may aid in therapeutic management and research strategies. Further studies are needed to more conclusively clarify the relationship between apathy, fatigue, depression, and anxiety but also cognition and motor status.The current study has several limitations. First, from a methodological view, a larger sample could have been split to first conduct an exploratory factor analysis and to test the resulting empirically informed clusters using confirmatory factor analysis on the second half of the data. However, the statistical power analysis revealed that to detect a small-to-medium effect size, the current sample was just large enough. Therefore, we refrained from splitting the data. Second, the HADS has been shown to measure general distress rather than anxiety or depression specifically  apart frchristian.ineichen@usz.ch.The data analyzed in this study is subject to the following licenses/restrictions: Institutional health related data. Requests to access these datasets should be directed to Christian Ineichen, The studies involving human participants were reviewed and approved by the cantonal ethics committee Zurich. The patients provided their written informed consent to participate in this study.CI performed the analyses and confirms that he has final responsibility for the decision to submit for publication. HB-V provided clinical expertise for statistical modeling. CI and HB-V wrote the paper. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Compared with thoracic breathing, abdominal breathing increased sway velocity and variability, and reduced complexity (p < 0.05). These findings show that balance ability is affected by breathing, even in healthy young adults.Recent studies have suggested that breathing type may affect balance ability. However, most of these studies were conducted on the elderly and patients with musculoskeletal or neurological disorders. Therefore, the effect of voluntary breathing, such as thoracic and abdominal breathing, on the balance ability of people in various age groups is not clearly understood. The purpose of this study was to investigate the differences in balance ability according to the type of breathing in healthy young adults. This study included 78 healthy, young adults. All subjects were assessed for balance ability in neutral breathing, thoracic breathing, and abdominal breathing through a crossover design. Balance ability was assessed during static standing using a force plate. Participants were trained in voluntary breathing, evaluated using electromyography. During voluntary breathing, sway velocity, anterior-posterior difference, and anterior-posterior standard deviation increased while anterior-posterior sample entropy decreased compared to neutral breathing ( Balance is the ability to maintain the center of gravity (COG) in the base of support (BOS) with minimum postural sway ,2,3,4,5.To maintain balance, the ability to perceive the body through various sensory organs is required . The visIn order to accurately evaluate balance ability, a previous study has suggested that the environment between measurements or between subjects be controlled, and generally recommend adjusting the ground and the subject’s measurement posture . In addiBalance ability is most commonly considered to evaluate the motor function, daily life movements, gait, and sports performance in various subjects , and undParticipants in this study were recruited through bulletin board advertisements and posters in community service centers and G university in I city. Participants were recruited for sedentary residents who did not a vigorous-intensity physical activity for 3 or more days per week, for 30 or more minutes ,15. The The sample size was calculated using G-power software  ,17. BaseThis study followed a randomized crossover design. A CONSORT flow diagram with a crossover design is shown in Training for voluntary breathing, that is TB and AB, was undertaken using real-time feedback from electromyogram (EMG) signals. During TB training, participants were instructed to: “Breathe to move the upper graph  and keep the lower graph  as flat as possible.”. Similarly, during AB training, participants were instructed to: “Breathe to move the lower graph  and keep the upper graph  as flat as possible.” Respiratory muscles consistent with the breathing type were to be maintained at higher activity than during NB. The rest of the muscles were to be maintained at a flat line with an activity that did not exceed the mean ±2 standard deviations (SDs) of NB activity. After voluntary breathing training, participants were asked to undertake breathing training for 1 week. After 1 week, voluntary breathing activity was assessed and balance ability was measured.Balance ability was measured with AccuSway , which showed high reliability (ICC for inter-rater and test-retest reliability = 0.70–0.89) . All outReal-time feedback via EMG signals was used to evaluate voluntary breathing during breathing training and measurements. EMG signals were collected with AcqKnowledge 5.0 . The band-pass filter was set to 30–500, and the sampling rate was set to 1000 Hz. The EMG signal was full-wave rectified. Electrodes were attached to the external intercostal muscles (between the 6th and 7th ribs), sternocleidomastoid muscle (1/3 of the distance between the mastoid process and the jugular notch), transverse abdominis muscle , and rectus abdominis muscle (2 cm below the anterior superior iliac spine) after hair removal and exfoliation with alcohol ,20.For all variables, the average value of the data was repeated three times and expressed as the mean ± SD. Statistical analysis was performed using SPSS statistical software . The Kolmogorov–Smirnov test was used to assess normal distribution, and non-parametric statistical methods were used for non-normal variables. Balance ability with each breathing technique was compared using the Friedman test followed by the Wilcoxon signed-rank test, and the significance level was corrected using the Bonferroni correction method. The critical value of significance was set at α = 0.05.The 78 subjects who participated in this study completed all the interventions and evaluations; there were no dropouts. The general characteristics of the participants are presented in p < 0.05). The anterior-posterior standard deviation was lowest in NB and highest in AB (p < 0.05). The anterior-posterior sample entropy was highest in NB and lowest in AB (p < 0.05). There was no significant difference between conditions in sway area, right-left difference, right-left standard deviation, and right-left sample entropy.The balance ability results for standing on both feet are presented in p < 0.05). The anterior-posterior difference was significantly smaller in NB than in TB or AB (p < 0.05). The right-left standard deviation was significantly smaller in NB than in TB or AB (p < 0.05). The anterior-posterior standard deviation was lowest in NB and highest in AB (p < 0.05). The entropy of the right-left sample was significantly higher in NB than in TB or AB (p < 0.05). The anterior-posterior sample entropy was highest in NB and lowest in AB (p < 0.05). There was no significant difference between the sway area and the right-left difference between the conditions.The results of the balance ability on one foot are shown in This study investigated the effect of voluntary breathing on the balance ability of healthy, young adults. It found that voluntary breathing affects balance ability in two-legged and one-legged standing in healthy, young adults and that AB has a greater effect than TB. Some previous studies have suggested that voluntary breathing can affect the balance ability of the elderly and patients, and our study also confirmed the same tendency in young adults. These results suggest that voluntary breathing may affect balance ability during static standing in the elderly and patients as well as young healthy adults, and it should be controlled to properly perform balance measurements.Manor et al.  defined When postural perturbation occurs, in addition to using a balance strategy to maintain postural stabilization, anticipatory postural adjustments (APA) could minimize the effects of postural perturbation . AdditioAn increase in respiratory muscle activity during voluntary breathing may affect the increase in sway. Voluntary breathing induces greater activation of respiratory muscles than in NB condition, and our results also showed that the activity of external intercostal muscle and sternocleidomastoid muscle was higher in TB than in NB . FurtherBalance ability has been most commonly used to evaluate the motor function of healthy adults, the elderly, athletes, and patients with the disorder in the clinic and various sports environments. In order to perform this evaluation more accurately, a previous study has recommended excluding as much as possible the influence of external factors that may affect postural balance and physical function, and to proceed with the evaluation by adjusting the subject’s foot position, arm posture, gaze, and measurement surface . While pThis study investigated changes in the balance ability of healthy, young adults during different breathing methods. However, our study has several limitations. First, the sample size was small; therefore, it is difficult to generalize the results. Second, we did not measure the subject’s foot size and width. Therefore, it is difficult to confirm the influence of individual differences according to foot size on postural balance changes following voluntary breathing. Third, when measuring muscle activity, the activity of other muscles was not known because the respiratory muscles were mainly measured. Fourth, it was not possible to control the respiration volume equally during the measurements. For this reason, it was not possible to confirm the difference according to the volume of respiration. Finally, we could not measure changes in posture, such as rib cage volume and changes in three-dimensional movements, during breathing. In order to generalize the study results, it is suggested that additional body measurements, evaluation of extremity muscles, and monitoring of respiratory rate and trunk movement should be performed in subsequent studies.The results of this study showed that there is a difference in the balance ability of healthy, young adults according to the type of breathing. In particular, it was found that AB had a greater effect on balance ability than other breathing methods. Therefore, the breathing method should be controlled for balance evaluations.