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Article

Beliefs and Behaviors: Mind-Body Health Influences on Health Behaviors Amidst COVID-19

by
Aarti P. Bellara
1,*,
Emily L. Winter
2,
Johanna M. deLeyer-Tiarks
3,
Adeline Bray
4 and
Melissa A. Bray
5
1
Department of Human Services, Western Carolina University, Cullowhee, NC 28723, USA
2
School of Health Sciences, Touro University, New York, NY 10036, USA
3
Psychology Department, Pace University (NYC Campus), New York, NY 10038, USA
4
Bay State Hospital, Springfield, MA 01199, USA
5
Neag School of Education, University of Connecticut, Storrs, CT 06269, USA
*
Author to whom correspondence should be addressed.
COVID 2025, 5(10), 169; https://doi.org/10.3390/covid5100169
Submission received: 12 August 2025 / Revised: 22 September 2025 / Accepted: 27 September 2025 / Published: 8 October 2025
(This article belongs to the Section COVID Public Health and Epidemiology)
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Abstract

In order to understand how health beliefs map onto health behaviors, a national survey, administered in the wake of the COVID-19 campus closures, was conducted to explore college students’ mind-body health beliefs and their health behaviors (across dimensions of physical exercise, diet/nutrition, and socialization). To this end, the Mind-Body Health Screener (MBHS), a five-item, Likert scale, brief measure, was developed. The present study applied an online survey design administered nationally to U.S. undergraduate students during the initial lockdowns with the pandemic (n = 557). To examine the psychometric properties of the MBHS, exploratory and confirmatory factor analyses were run as well as measures of reliability. Furthermore, linear regressions and effect sizes were computed to understand the connection between mind-body health beliefs and behaviors. While initial data supported the psychometric properties of the Mind-Body Health Screener (MBHS) developed for this purpose, substantive results suggested that mind-body health beliefs did not relate to mind-body health behaviors (either before or after the campus closures), aligning with the Cognitive Dissonance Theory. Post hoc analysis did, however, suggest a significant change in health behaviors from pre-campus closures to during the closures, suggesting students engaged in more physical exercise, eating behaviors, and socializing before campus closed, observed with small to large effects. Taken together, the findings of the present study illustrate how the Cognitive Dissonance Theory is a relevant perspective to consider the relation between health beliefs and behaviors during a period of immense stress, such as the COVID-19 initial campus closures.

1. Introduction

The integration of mind and body is a topic of relevant interest across fields—especially within the medical and health fields, where healthcare providers seek to help people change their behaviors to engage in healthier choices. With the connection between mind and body, providers may be curious as to whether their patients hold values rooted in health and well-being, and if so, if those beliefs translate over into action. Mind-body health behaviors are critically important in high-stress situations where engaging in healthy habits is beneficial both for “getting through” the moment and for long-term health. To this end, the present study sought to understand the connection between mind-body health beliefs and mind-body health behaviors of young adults during the stressful and uncertain times of the COVID-19 pandemic, through measuring both mind-body health beliefs and subsequent behaviors. The team hypothesized that beliefs and behaviors would correlate during the COVID-19 pandemic. Additionally, this study also explored the survey development process of the Mind-Body Health Screener (MBHS), with the research team hypothesizing that the measure would demonstrate evidence of reliability. In what follows, we present existing literature on mind-body health, health beliefs, health behavior theories, and how COVID-19 served as a ripe catalyst for understanding undergraduate students’ stress.

1.1. What Is Mind-Body Health?

Mind-body health (MBH) focuses on the integration of psychological and physical aspects of well-being [1]. Situated at the crux of integration and exchange, mind-body health considers “the seamless interaction of the mind, or psyche, with the physical body to result in the overall functioning of the individual” [1]. For instance, physical health interventions for improving psychological outcomes may be targeted, or vice versa, psychological interventions aimed at improving physical or psychological health outcomes. Interventions are well-suited for various settings [2], including education, medical, and mental health contexts, with supports designed to help individuals with a variety of academic, behavioral, or social-emotional concerns, supporting people across various domains of functioning. MBH techniques can be used within both the stances of prevention and intervention, focusing on aligning an intervention and the details around the frequency of dosing to match the specific needs of the patient. Beyond supporting patients, clients, and students directly, MBH benefits mental health practitioners, school-based professionals, and medical service providers, especially those at risk of burnout [3].
Regarding interventions, MBH utilizes non-invasive techniques to target behaviors, physical outcomes, academic performance, and emotional well-being. Specifically, interventions span the following dimensions: (1) positive psychology and multidimensional adjustment, (2) mindfulness-based interventions, (3) mindful gratitude, (4) relaxation and guided imagery, (5) physical activity, (6) yoga, (7) expressive arts, (8) music, (9) written emotional expression, (10) video self-modeling and virtual reality, (11) hypnosis, (12) emotional freedom techniques, (13) eco health and nature, (14) meditation, and (15) standardized progressive muscle relaxation [4]. Many strategies are evidence-based, as shown by one comprehensive and thorough review of the research [5] demonstrating improved overall health [6] and perceived happiness [7]. Of note, some MBH interventions are supported by small amounts of emerging evidence (e.g., hypnosis, emotional freedom techniques) [8,9], interventions which were not addressed in the present research. For more detailed information regarding the implementation of mind-body health strategies, see Maykel and Bray’s [10] Promoting Mind-Body Health in Schools: Interventions for Mental Health Professionals.
Understanding students’ beliefs regarding the efficacy of MBH interventions is a vital consideration when deciding to develop and implement programming in higher education settings. In order to successfully implement MBH interventions within the college setting, it is necessary to gain insight into the views that college students may have towards MBH. Research findings have suggested that if college students perceive that MBH interventions can lead to positive outcomes, they will be more likely to take part in them [11].

1.2. COVID and Mind-Body Health

The COVID-19 pandemic greatly affected the physical and mental health of individuals around the world. The result of lockdowns and social isolation led to many mind-body health issues, such as substance use [11], anxiety and panic, depression, loneliness, obsessive compulsive disorder, and post-traumatic stress disorder [12], developing its own term, “COVID Stress Syndrome.” In short, the pandemic stress not only exacerbated existing mental health impairments but also created new psychological challenges that were not present prior to 2020. Cross-sectional research during lockdowns suggested overarching themes rooted in concerns for the mental health of students [13]. Students reported increased symptoms of depression, anxiety, and suicidality [13,14], disproportionality impacting students from various minoritized backgrounds [15]. Furthermore, students reported feeling ill-equipped to cope with their emotions and expressed concerns surrounding their academic performance, physical health, and lifestyle [13,16]. In short, the decision to prioritize evacuating, quarantining, social distancing, and transitioning to remote learning in order to keep students, faculty, and staff healthy had unintended consequences related to student mental health and support services [17].
Furthermore, COVID stress impacted people’s physical health due to lack of exercise, overeating, poor nutritional access or choices, and reduced socialization [18]. Regarding physical health, schools, exercise classes, gyms, and fitness centers were closed, reducing access to services or changing physical health routines and motivation [18].
The negative mental and physical consequences of COVID were hugely impactful. Specifically, college-age individuals faced a toll as undergraduate students’ experiences were halted; in-person socialization was brought to near zero, and the damage was harsh [19]. Given the unique features of the COVID-19 campus closures, we felt it was an interesting time to assess the connection between MBH beliefs and behaviors, given the immense period of unprecedented stress students faced. Thus, this precarious time, while scary and stressful, provided a naturally appropriate setting, where the use of MBH practices would have been helpful to many to assess the alignment between MBH beliefs and behaviors.

1.3. Cognitive Dissonance and Health Beliefs Model: Beliefs Versus Behaviors

The relation between health knowledge, health-related beliefs, and health-related decisions and behaviors has been the subject of research for over 50 years. While it has been demonstrated that health knowledge promotes the acquisition of healthy behaviors, research on the connection between health knowledge, health-related attitudes, and beliefs has not sufficiently demonstrated that health knowledge is enough to predict a person’s health-related beliefs and attitudes during a pandemic [20]. In its original definition, the Health Belief Model (HBM) [21,22,23] indicated that patients’ health-related beliefs and value systems guide their healthcare decisions and health-related behavior. The model has expanded to recognize multiple critical cognitive elements: (1) perceived susceptibility, (2) perceived severity, (3) perceived benefits, (4) perceived barriers, (5) self-efficacy, and (6) cues to action [24]. Within scholarship related to the HBM and COVID-19, the model has been used to understand the health beliefs that influence one’s intentions to receive the COVID-19 vaccination in an international sample [25].
However, psychological research on cognitive dissonance suggests that beliefs are not sufficiently predictive of behavior and that beliefs change over time in response to behavior [26,27]. When individuals experience cognitive inconsistency, such as engaging in behaviors that do not align with their beliefs, the psychological phenomenon of cognitive dissonance occurs. Dissonance theory [28] postulates that holding two conflicting thoughts, such as a smoker’s knowledge that smoking cigarettes is unhealthy alongside the knowledge that they smoke, causes psychological discomfort, termed cognitive dissonance. Subsequent scholarship on action-based models of cognitive inconsistency further explains the connection between beliefs and behaviors by positing that individuals will not only change what they believe in response to their behavioral decisions but will go further to adjust the relative value of their beliefs to justify how they have behaved [27]. Decades of research on cognitive dissonance and related theory shows that cognitive inconsistency and thought–behavior incongruence motivate individuals to change their thoughts, behaviors, or both so that their cognitions, including their values and beliefs, are in alignment with their actions [28,29,30]. Recent research on the role of cognitive dissonance on beliefs and behaviors during the COVID-19 pandemic has shown that dissonance arising from racial identity and societal stigma moderates both health-related beliefs (i.e., mistrust in public health information disseminated by governmental and health agencies) and affiliation with cultural group norms, including health-related behaviors related to cultural group norms (e.g., wearing a mask in public as a culturally derived familial duty among immigrants of Asian descent) [31,32]. In sum, individuals will change what they think in accordance with their behaviors and change how they behave in accordance with what they think. For a full discussion of cognitive dissonance and related theories of psychological change, please see Harmon-Jones [33].
More recent research on patients’ health-related behaviors, values, and beliefs has produced revisions to the Health Belief Model [34] that account for the reciprocal nature of belief and behavior in the prediction of patients’ health-related decision-making and resulting behaviors. Additionally, research on the connection between patients’ emotions, health beliefs, and health knowledge has revealed that emotions are a salient factor contributing to health-related decision-making during the COVID-19 pandemic [35]. Illustrating the reciprocal nature between emotions and health-related behavior are Deacon et al. [36] and Olatunji and colleagues’ [37] investigations of excessive health-safety behaviors (e.g., frequent hand washing, frequent heart rate monitoring, avoidance of touching money) and hypochondriacal beliefs. Their research showed that after psychologically healthy participants were instructed to engage in a high degree of health-safety behaviors for just one week, their fears of contamination and health-related anxiety substantially increased [36,37]. Such findings have important implications for our understanding of individuals’ health-related beliefs.
As noted, the COVID-19 lockdown was a time of significantly heightened stress, particularly for college and university students [13]. It is well-documented that psychological stress predicts the likelihood that people will engage in unhealthy behaviors like increased smoking and alcohol use and decreased physical activity and diet quality in adolescents and young adults [38,39]. Furthermore, stress is associated with impulsive decision-making that favors immediate gratification over long-term rewards of higher magnitude, regardless of beliefs, goals, and values [40]. When investigated within the context of COVID-19-related stressors, such findings have held. For instance, in their study on COVID-19 stress and impulsivity, Thibault et al. [41] concluded that stressors caused by the COVID-19 virus and COVID-19 lockdown were associated with impulsive health-related decision-making (i.e., alcohol use). Taken with the understanding that health-related knowledge does not reliably predict health-related beliefs, and that people alter their beliefs to align with their behaviors, psychological stress may serve as a predictor of altered and maladaptive health-related beliefs. While this body of research is still emerging [42], research has demonstrated that high levels of chronic stress predict COVID-19 health beliefs, specifically, conspiracy beliefs [43,44]. Such findings are indicative of the direct relation between psychological stress and health-related beliefs and are of applied importance because COVID-19 conspiracies are associated with reduced compliance with public health safety measures [45]. As such, it is critical to conduct research on the relation between health-related beliefs and health behaviors in the context of pervasive, population-level psychological stress.

2. Materials and Methods

2.1. Design, Participants, and Data Collection

The present study used an online survey (administered via Qualtrics) to assess the psychometric properties of the MBHS and to investigate respondents’ mind-body health beliefs and behaviors. Convenience sampling methodology was applied as a means to collect responses from undergraduate students attending United States four-year residential colleges/universities. The goal of the larger survey was to gain an understanding of the undergraduate experience amid the initial shutdowns of COVID-19. A Qualtrics link was emailed to faculty volunteering to share the survey via their institution’s student communication listservs, or their course learning management sites. Snowball and convenience sampling methods were used to allow respondents to pass along the study to their peers through either direct emails, social media, or university website/listserv forwarding. Given the sampling methodology, it was not feasible to track or calculate the response rates by a student’s university given the anonymous setup of the survey.
All data were collected between May 2020 and August 2020. Respondents provided an email address (which was not linked to their responses) in a random drawing for one of 100 USD 20.00 gift cards. In total, 727 individuals opened the survey, 726 consented to participate, and 557 students completed the MBHS. Listwise deletion was used in SPSS Versions 26 and 28 to select respondents who completed both the screener and mind-body health use questions.
Approval from the University of Connecticut’s Institutional Review Board was obtained under exempt status, protocol #X20-0088. Participants provided consent virtually prior to initiating the anonymous online survey, where they could leave questions unanswered if they chose and were welcome to exit the survey at any point. Participants had the opportunity to enter an optional raffle via providing an email address, which was not connected to their responses. Not all participants participated in the raffle.

2.2. Measure

The Mind-Body Health Screener (MBHS) items were developed by the second, third, and fifth authors, who are content experts, and edited by the first author. The five items included the following: (1) It is important to have a healthy mind in order to have a healthy body, (2) It is important to have a healthy body in order to have a healthy mind, (3) It is important to have a healthy mind and healthy body in order to do well academically, (4) It is important to have a healthy mind and healthy body in order to do well socially, and (5) It takes practice and effort to maintain a healthy mind and healthy body.
To assess use of mind-body health strategies both before and during the pandemic, six questions were included: Before campus was closed due to COVID-19, (1) How likely were you to engage in physical exercise, (2) How likely were you to engage in healthy eating habits, and (3) How often were you socializing (in person, going to events/activities) with your peers? Furthermore, since campus has been closed due to COVID-19, (1) How often are you engaging in physical exercise, (2) How often are you engaging in healthy eating habits, and (3) How often are you socializing (e.g., online, phone, video chat) with your peers?

2.3. Data Analysis Procedures

Analytical techniques aligned with the two-pronged purpose of the study: to examine the relation between undergraduate students’ beliefs about mind-body health during the COVID-19 pandemic and their health behaviors, while also assessing the psychometric properties of the MBHS. The psychometric analysis of the MBHS is presented first to provide evidence for the subsequent use and interpretation of scores derived from the measure. Analyses were completed in SPSS Statistics Versions 26 and 28 as well as R software and R Studio version 4.4.
To evaluate the psychometric properties of the MBHS, the team randomly split the data in half and used half in an exploratory factor analysis (EFA) and half in a confirmatory factor analysis to explore, and subsequently confirm, the underlying factor structure. Missing response patterns were first examined, and no evidence of systematic patterns that may explain missingness were found. Next steps proceeded to use a list-wise deletion for any missing responses, resulting in 280 responses for the EFA. Given the screener only had five items, it proceeded forward with a principal axis factoring (PAF) for a single factor with no rotation. The data were evaluated against the Kaiser–Meyer–Olkin (KMO) test and Bartlett’s test of sphericity. The KMO test compared the magnitude of the observed correlation coefficients to magnitudes of partial correlation coefficients for each item, while controlling for all of the other items in the correlation matrix [46,47]. Bartlett’s test of sphericity provided information about whether there was a relation among the items in the instrument [48]. Next checked were the communalities, which provided information about the percentage of variance explained across the extracted factors [49]. Finally, Cronbach’s alpha was computed for these responses to evaluate the internal consistency or the reliability of the scores.
To examine the relation between MBH beliefs and behaviors, a series of linear regressions were conducted between students’ responses for the total MBHS score and questions assessing students’ use of MBH strategies both before and during the COVID-19 campus closures.

3. Results

In order to understand the connection between mind-body health beliefs and mind-body health behaviors during the COVID-19 pandemic, mind-body health beliefs and subsequent behaviors were assessed through a nationally administered online survey. A total of 557 students completed the five-item MBHS (see Table 1). The 5-point Likert-type scale ranged from strongly disagree to strongly agree, with values of 1 aligning with strongly disagree and values of 5 with strongly agree (range: 5–25). There were 17 different unique scores, with four students scoring as low as a 5, and 105 students scoring a 25. The modal score was 25, occurring in 19% of the respondents. The distribution of scores was negatively skewed, with 58% of the scores at 20 or higher. Individual items were leptokurtic with a negative skewness (see Table 2 and Figure 1). The negative skewness suggests that outliers that disagreed with the statements impacted the skew of the data. These extreme values are noted with the leptokurtic distribution.

3.1. Evidence of Reliability

Cronbach’s alpha for the scale was 0.824 (95% CI = 0.789–0.854), a strong alpha for a scale with only five items. When considering whether to delete any item, the Cronbach’s alpha value would decrease if any items 1 through 4 were removed. If item 5 was removed, the coefficient would not change, suggesting that it would be beneficial to retain these items in future use of this measure.

3.2. Exploratory Factor Analysis

The screened data were analyzed via SPSS Version 28 to conduct the exploratory factor analysis and estimate the reliability of 280 responses from undergraduate students. The KMO test, which compares the magnitude of the observed correlation coefficients to magnitudes of partial correlation coefficients for each item while controlling for all the other items in the correlation matrix, was 0.795. Bartlett’s test of sphericity also returned a significant result (χ2 = 509.30, p < 0.001). The communality for item 5 was low (see Table 3 and Table 4); however, we proceeded with the item to see how it performed in concert with the other items. The MBHS contained five items, with a hypothesized single-factor solution. Factor loadings ranged between 0.400 and 0.825, suggesting that the items are moderately related to the underlying factor, supporting a unidimensional scale. Although the ideal factor loading is 0.500 and above [46,49], factor loadings as low as 0.400 may be retained. Coupled with the reliability evidence suggesting that removal of the item would decrease the evidence of reliability, along with the theoretical considerations of mind-body health, the authors decided to retain item five. The authors also recognize that this factor loading is the minimally acceptable loading [46]. Hence, with evidence of reliability and validity of the measure established, the measure was justified for use in further analysis.

3.3. Confirmatory Factor Analysis

A confirmatory factor analysis (CFA) was conducted in R Software and R Studio, version 4.4 using the lavaan package. Results for the hypothesized model were evaluated and then compared to an alternate model. The data were fitted to the model in lavaan using the CFA command, and summary results, the standardized solution, normalized residuals, and modification indices were calculated for the fitted model. The root mean square error of approximation (RMSEA) is an absolute measure of fit, while the comparative fit index (CFI) and the Tucker–Lewis index (TLI) are both incremental measures of fit. Absolute measures of fit simply examine if the model fits, while incremental measures of fit compare the fitted model to the null model to examine relative or incremental fit [50,51]. For the RMSEA, a result of 0.01 is considered excellent, while a result above 0.1 is often the cut-off for poorly fitting models. In addition, it is recommended that the lower limit of the RMSEA confidence interval is close to zero and the upper limit is less than 0.08. The CFI should be interpreted in conjunction with the RMSEA. Recommended CFI results for good model fit should be above 0.95 with results less than 0.9, which would indicate poor fit. If the results are close to 0.9, there must be a strong RMSEA. The TLI is also interpreted with the RMSEA, and the threshold for adequate fit is 0.90 [50]. The CFA indicated that the five-question screener did not yield adequate model fit according to the Model Fit Indices in Table 5.

3.4. Mind-Body Health Screener and Usage

In order to understand the relation between mind-body health beliefs and behaviors, participants responded to six questions evaluating their personal use of mind-body health behaviors, with three of the questions related to use prior to the campus closures and the other three assessing behaviors since the closure. Frequencies for respondents’ use of mind-body health strategies prior to and during the COVID-19 campus closures are highlighted in Table 6. Three linear regressions revealed no significant relation between their belief scores as measured by the MBHS and their post-campus closure behaviors (see Table 7). This pattern was similar when assessing beliefs and behaviors before campus closures, again yielding non-significant findings. Dependent means t-tests were conducted as post hoc analyses in order to compare the patterns of responses for MBH behaviors before and after the campus closures (see Table 8). Findings suggested that participants indicated they more frequently engaged in MBH behaviors prior to the campus closures. After the Bonferroni adjustment (ɑ = 0.016), results continued to be statistically significant for all three items: for physical exercise with small effect (Mean Difference = 0.503, Cohen’s d = 0.442 [95% CI: 0.348, 0.535]), eating behaviors with a small effect (Mean Difference = 0.279, Cohen’s d = 0.276 [95% CI: 0.185, 0.367]), and socializing with large effect (Mean Difference = 1.083, Cohen’s d = 0.876 [95% CI: 0.769, 0.980]).

4. Discussion

The results of the present study, specifically the disconnect between mind-body health beliefs and mind-body health behaviors, highlight similar patterns observed by medical providers in research, that health beliefs do not always align with health behaviors, especially during a pandemic [20]. These data contradict what one would expect to find given the theorizing of the Health Belief Model [21,22,23], with findings instead aligning better with the Cognitive Dissonance Theory perspective, suggesting a split between beliefs and behaviors for which one must cognitively cope [33]. Specifically, our findings demonstrated that beliefs about one’s health did not predict (as demonstrated by a regression) one’s health behaviors; perhaps this disconnect with the HBM is unique in highly stressful and novel situations, such as the COVID-19 pandemic. Research supports the notion that stress can impact one’s cognitive functioning generally (e.g., cognitive flexibility, behavioral inhibition, working memory) [52], as well as during the COVID-19 pandemic specifically, with these findings highlighted in a systematic review [53]. For instance, one U.K. based study examining cognitive functioning prior to and during COVID highlighted decreased executive functioning skills and working memory abilities [54]. These findings highlight that the experience of the pandemic (regardless of experiencing infection directly) impacted cognitive functioning. These findings may explain why behavioral changes during the school closures did not lead beliefs to impacts on behaviors, suggesting that stress likely inhibited cognitive adjustments. These data suggest that although undergraduate students may inherently have believed in the connection and importance between the mind and body as well as be conceptually interested in health behaviors, these beliefs did not immediately translate over into action regarding health-focused behaviors, a pattern reflecting the impact of cognitive dissonance in stressful circumstances.
These findings were surprising, given the original hypotheses, as the authors suspected that a widespread health crisis (such as the pandemic) would impact students’ perceptions of the health threat and thereby, their actions/values aligning around health behaviors [24]. At the same time, critics of the model have long proposed that it may underestimate the emotional/social factors (while emphasizing the cognitive ones), overlooking cultural and social considerations [24]. Results from the present study also highlighted that students engaged in less mind-body health behaviors prior to the campus closures, as compared to during the campus closures (with small to large effect sizes), demonstrating a change in health behaviors during the initial outset of the pandemic.
What makes these findings most interesting, perhaps, is this perspective about behaviors and beliefs within a period of immense stress, such as that of the COVID-19 campus closures. Especially for the population of the present study, college-aged students, this initial campus closure period was one of immense change and uncertainty [55,56]. Prior research suggests that when undergoing periods of psychological stress, individuals are actually more likely to engage in unhealthy behaviors [38,39], with the preference towards immediate gratification [40]. The present findings align with emerging reports in the literature that stress may be a predictor of maladaptive health behaviors [42], specifically related to the statistically and practically significant differences in mind-body health behaviors prior to and during the campus closures as students engaged in less health-driven behaviors.
Additionally, from a measurement development perspective, these findings suggest preliminary evidence for use of the MBHS among college students. More specifically, CFA results indicated additional areas for refinement are likely needed, perhaps to add questions to support the use of the MBHS as a standalone measure of mind-body health beliefs. Nonetheless, the preliminary evidence supports the use of this screener to ascertain college-aged students’ mind-body health beliefs, when considering programmatic planning and potential interventions. Additional practical implications of this work are discussed below.

4.1. Practical Considerations

The findings of the present study further contribute to the intersection between the Health Belief Model and Cognitive Dissonance Theory, as well as considerations of what people do when they are under stress. Practically speaking, these findings suggest that assessing for mind-body health beliefs may not be a great predictor of someone’s behavior, or at least, not be a great predictor when someone is under stress. For mental health practitioners and medical providers alike, these findings reinforce the importance of holistically understanding stressors in a person’s life when speaking about health behavior change. Furthermore, when intervening to offer support to reduce cognitive dissonance, such as through motivational interviewing (MI), a brief, evidenced-based psychological intervention aimed at enhancing ambivalence can help to facilitate change. Use of MI has extended to health care settings, with ample research supporting its use to “optimize medical interventions” [57], p. 109 within medical offices and community clinics. Beyond the direct use of MI, the present findings highlight that it might be prudent for providers to assess patient stress on personal, societal, and systemic levels to guide conversations to actively discuss how stressors may lead to barriers or further ambivalence, aligning with the MI strategy use and general process of MI counseling.
From a measurement development perspective, this research supports universities’ use of the five items in the screener (supplemented by additional mental health assessment tools) to understand what comprehensive health services may be beneficial to undergraduate students. Such a measure in MBH-related services could provide information on which student affairs services to expand, or cut, based on students’ beliefs about mind-body health. A low total score on the screener may suggest the need for empirically supported mind-body health prevention and intervention services. On the other hand, if the total score is high, the results provide helpful insight to guide treatment interventions that may be meaningful given the student’s perspective on mind-body health and their receptiveness to the mental health and wellness programming.

4.2. Limitations

The present study was completed in the wake of the COVID-19 pandemic. Given this time period, the data collected were undoubtedly influenced by the immediate crisis occurring at that moment in time. Thus, it is important to consider that how people respond during a period of crisis may be different than their “baseline” or if these differences between their behaviors and attitudes may shift in a post-pandemic period. Regardless, the findings continue to be relevant, especially in the present study, when posed as research questions within the context of COVID-19 specifically.
An additional limitation includes potential bias in response rate, a common concern with survey design research. Furthermore, students were asked to retrospectively rate personal levels of engagement in physical exercise, eating, and socialization habits that they perhaps did not recall correctly, thereby under- or overestimating their pre-pandemic behaviors—susceptible to biases such as memory errors and social desirability—an important factor to consider. That said, the survey was administered very early in the pandemic (spring to summer of 2020), so it was in recent memory for many respondents. Next, while these data included robust quantitative measures, the team did not offer respondents a chance to fill in blank responses regarding why they believed in, or engaged with, health behaviors. In other words, the data are missing potentially rich qualitative information to supplement the statistics, a potential consideration for future research.
A final limitation of the present study concerned the sample, namely that the sample was of undergraduate U.S. college students. Thus, the findings are likely most translatable to the young adult/adolescent age group within the United States and thereby should be used with caution if generalizing to other age groups. Additionally, given the use of convenience and snowball sampling methodology, which possesses its own limitations, the approach did not include mention of the schools represented in the sample, which could have offered additional demographic context.
Specific to improvements to the MBHS, the anchors ranged from Never to Always. It may have been more useful to offer a well-defined scale that centered on the frequency of engagement in behaviors, such as 1–2 times a week, 3–4 times a week, or 5+ times a week, to concretely offer quantities instead of vague anchors. Furthermore, regarding the MBHS, future scholars should consider adding additional questions to increase the psychometric robustness of the measure, thereby increasing the evidence of reliability and validity.

4.3. Future Directions

Given these findings, future researchers may be interested in replicating this study in medical training settings to expand the understanding between mind-body health behaviors and beliefs in a wider sample of people (beyond the college undergraduate population), as well as to directly generalize to medical students. Furthermore, the survey did not ask about international student status, so future research may be interested in collecting that information to understand the cultural nuances between health beliefs and behaviors depending on individual differences between people and groups. Additionally, future research may consider examining the connection between mind-body health beliefs and behaviors from a mixed-methods approach to integrate robust quantitative data with insightful qualitative information and to understand the “why” behind certain alignments and discrepancies in mind-body health beliefs and behaviors.

5. Conclusions

What we believe about health, specifically the connection between the mind and the body, may not translate directly over to how we act, whether through exercise patterns, eating, or socialization, a finding highlighted in the present study suggesting that college students during the COVID-19 campus closures did not find their beliefs about health to translate to their behaviors across various dimensions of health: nutrition, exercise, or social well-being. The present study’s findings, aligning with Cognitive Dissonance Theory, suggested that for undergraduate students, their beliefs and behaviors did not relate both for behaviors reported pre- and post-campus closures. Interestingly, students engaged in less health-related behaviors during the closures, an immense period of stress, perhaps speaking to a moderating role of stress as related to the connection between health beliefs and health behaviors. Given that U.S. college students reported engaging in less health behaviors (with small to large effect) during the campus closures as compared to prior to the campus closures, these findings highlight the criticality of understanding stress in one’s life when seeking to understand the relation between their beliefs and behaviors—a relevant takeaway for providers to recognize when assessing readiness for health behavior change.

Author Contributions

Conceptualization, A.P.B.; methodology, A.P.B. and E.L.W.; validation, A.P.B.; formal analysis, A.P.B. and E.L.W.; data curation, A.P.B. and M.A.B.; writing—original draft preparation, A.P.B., E.L.W., J.M.d.-T., A.B. and M.A.B.; writing—review and editing, A.P.B., E.L.W. and M.A.B.; visualization, A.P.B. and E.L.W.; supervision, A.P.B.; project administration, A.P.B.; funding acquisition, M.A.B. All authors have read and agreed to the published version of the manuscript.

Funding

This study was privately funded through the fifth author’s research funds.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of The University of Connecticut (protocol number X20-0088; 13 May 2020).

Informed Consent Statement

Informed consent was obtained from all respondents involved in the study.

Data Availability Statement

The data presented in this study are available on request from the second author due to the Institutional Review Board data retention agreements.

Acknowledgments

Thank you to Lauren Klein and Katherine Nelson for their support in the survey development process. We extend our deepest gratitude to Marjorie Jeanine “Jean” Romano, who made valuable contributions to this project. Jean passed away before this paper was published, and we dedicate it to her memory.

Conflicts of Interest

The authors declare no conflicts of interest.

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Covid 05 00169 g001
Figure 1. Mind-Body Health Screener Score Distribution.
Figure 1. Mind-Body Health Screener Score Distribution.
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Table 1. Demographic Information from Respondents Completing the Mind-Body Health Screener.
Table 1. Demographic Information from Respondents Completing the Mind-Body Health Screener.
Demographic VariableTotalNPercentage
Age187613.6%
1910418.7%
2012923.2%
2110218.3%
22305.4%
23101.8%
2450.9%
25+81.4%
Missing/did not respond9316.7%
Male9517.1%
Female36665.7%
GenderNon-Binary/Gender Fluid/ Gender Non-Conforming71.3%
Transgender20.4%
Missing/Did not respond9216.5%
RaceAmerican Indian/Alaskan Native20.4%
Asian10418.7%
Black or African American264.6%
Native Hawaiian or Other Pacific Islander10.2%
White27549.4%
Multi-racial305.4%
Prefer not to answer264.6%
Missing/did not respond9316.7%
EthnicityHispanic/Latin Origin5610.1%
Not Hispanic/Latin Origin40572.7%
Missing/Did not respond9616.3%
Table 2. Item Statistics for Mind-Body Health Screener.
Table 2. Item Statistics for Mind-Body Health Screener.
ItemsMeanSDSkewKurt
1. It is important to have a healthy mind in order to have a healthy body.4.500.757−2.105.95
2. It is important to have a healthy body in order to have a healthy mind.4.080.902−1.111.18
3. It is important to have a healthy mind and healthy body in order to do well academically.4.130.909−1.161.22
4. It is important to have a healthy mind and healthy body in order to do well socially.4.130.867−1.241.79
5. It takes practice and effort to maintain a healthy mind and healthy body.4.650.617−2.449.07
Table 3. Communalities from a 1-Factor PAF Solution.
Table 3. Communalities from a 1-Factor PAF Solution.
ItemsInitialExtraction
1. It is important to have a healthy mind in order to have a healthy body.0.3770.418
2. It is important to have a healthy body in order to have a healthy mind.0.4270.506
3. It is important to have a healthy mind and healthy body in order to do well academically.0.5310.578
4. It is important to have a healthy mind and healthy body in order to do well socially.0.5690.675
5. It takes practice and effort to maintain a healthy mind and healthy body0.2900.290
Table 4. Factor Loadings and Error Variances.
Table 4. Factor Loadings and Error Variances.
ItemsStandardized Factor LoadingsStandardized Error Variances
1. It is important to have a healthy mind in order to have a healthy body.0.5270.385
2. It is important to have a healthy body in order to have a healthy mind.0.6450.474
3. It is important to have a healthy mind and healthy body in order to do well academically.0.8250.262
4. It is important to have a healthy mind and healthy body in order to do well socially.0.8110.288
5. It takes practice and effort to maintain a healthy mind and healthy body0.4000.282
Table 5. Model Fit Indices.
Table 5. Model Fit Indices.
Fit IndexIndex Value
Model Chi-square Test
Test Statistic36.824
Degrees of Freedom5
p-value0.000
Comparative Fit Index (CFI)0.921
Tucker–Lewis Index (TLI)0.843
Root Mean Square Error of Approximation
RMSEA0.154
90% CI Lower Bound0.110
90% CI Upper Bound0.202
Standardized Root Mean Square Residual0.064
Table 6. Frequency Count for MBH Usage Prior to and During COVID-19 Campus Closures.
Table 6. Frequency Count for MBH Usage Prior to and During COVID-19 Campus Closures.
Before Campus Was Closed Due to COVID-19Since Campus Has Been Closed Due to COVID-19
ItemNeverRarelySometimesUsuallyAlwaysNeverRarelySometimesUsuallyAlways
How often are you engaging in physical exercise? 7391311341711910113213592
How often are you engaging in healthy eating habits?4201402259376316716083
How often are you socializing (e.g., online, phone, video chat) with your peers? 12465150242141201848973
Table 7. Regression Results: Mind-Body Health Beliefs and Behaviors.
Table 7. Regression Results: Mind-Body Health Beliefs and Behaviors.
QuestionBSE BβtprR2
Before campus was closed due to COVID-19
How likely were you to engage in physical exercise?0.0890.0760.0541.1800.2390.054--
How likely were you to engage in healthy eating habits?0.1380.0600.1042.2850.023 **0.1040.011
How often were you socializing (in person, going to events/activities) with your peers? 0.0420.0650.0300.6490.5170.030--
Since campus has been closed due to COVID-19
How often are you engaging in physical exercise? 0.1250.0830.0691.5030.1330.069--
How often are you engaging in healthy eating habits?0.1170.0710.0751.6350.1030.075--
How often are you socializing (e.g., online, phone, video chat) with your peers? −0.0760.078−0.044−0.9700.332−0.044--
Note. ** = Not significant after adjusting for Bonferroni adjustment (ɑ = 0.0083), B = unstandardized regression coefficient, SE B = standard error of B, β = standard coefficient.
Table 8. Difference in Mind-Body Health Behavior Prior to and During Campus Closures.
Table 8. Difference in Mind-Body Health Behavior Prior to and During Campus Closures.
QuestiontMean DifferenceCohen’s d (95% CI)
How likely were you to engage in physical exercise?9.67 *0.5030.442 (0.348, 0.535)
How likely were you to engage in healthy eating habits?6.04 *0.2790.276 (0.185, 0.367)
How often were you socializing (in person, going to events/activities) with your peers? 19.17 *1.0830.876 (0.769, 0.980)
Note. * = p < 0.001 significant after adjusting for Bonferroni adjustment (ɑ = 0.0167), degrees for question 1 were 478 and for questions 2 and 3 were 479.
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Bellara, A.P.; Winter, E.L.; deLeyer-Tiarks, J.M.; Bray, A.; Bray, M.A. Beliefs and Behaviors: Mind-Body Health Influences on Health Behaviors Amidst COVID-19. COVID 2025, 5, 169. https://doi.org/10.3390/covid5100169

AMA Style

Bellara AP, Winter EL, deLeyer-Tiarks JM, Bray A, Bray MA. Beliefs and Behaviors: Mind-Body Health Influences on Health Behaviors Amidst COVID-19. COVID. 2025; 5(10):169. https://doi.org/10.3390/covid5100169

Chicago/Turabian Style

Bellara, Aarti P., Emily L. Winter, Johanna M. deLeyer-Tiarks, Adeline Bray, and Melissa A. Bray. 2025. "Beliefs and Behaviors: Mind-Body Health Influences on Health Behaviors Amidst COVID-19" COVID 5, no. 10: 169. https://doi.org/10.3390/covid5100169

APA Style

Bellara, A. P., Winter, E. L., deLeyer-Tiarks, J. M., Bray, A., & Bray, M. A. (2025). Beliefs and Behaviors: Mind-Body Health Influences on Health Behaviors Amidst COVID-19. COVID, 5(10), 169. https://doi.org/10.3390/covid5100169

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