Impact of Exam Stress and Time of Day on Hand Grip Fatigue in Medical Students

Impact of Exam Stress and Time of Day on Hand Grip Fatigue in Medical Students | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Impact of Exam Stress and Time of Day on Hand Grip Fatigue in Medical Students Dominic Giandonato, Nicholas Rincon, Nathan Adamietz, Mihail Mitov This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6985107/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted 22 You are reading this latest preprint version Abstract Medical schools are using summative examinations to evaluate the students’ knowledge and competencies acquired in various biomedical and clinical domains throughout medical education. These examinations are necessary, but they are known to evoke high levels of stress in the students involved. Previous studies have shown that increased stress levels can negatively impact participants' cognitive and physical abilities, resulting in a decrease in overall performance. Our study was designed to test this relationship by comparing student hand grip strength output and fatigue rates using grip strength on stressful (exam) and non-stressful (non-exam) days. First-year medical students were recruited to squeeze a hand dynamometer at maximum muscle tension for 30 seconds three separate times. Students gave these readings on four separate days, including: morning examination/non-examination and afternoon examination/non-examination. Data sets were completed using a Vernier Go Direct Hand Dynamometer and Vernier Graphical Analysis Pro program. Statistical analysis was initially completed using GraphPad Prism, where the slope, the area under the curve (AUC), maximum, and average force were initially analyzed. The experimental data were further analyzed using analysis of variance (ANOVA), fitted with simple regression lines for the slope of fatigue for each pair of conditions, and a mixed linear model was created and tested using R statistical software. A total of 207 recorded hand grip profiles from 21 students were collected; 12 students completed the entire study (provided all measurements consisting of four days of 3 data sets each). These results showed no statistical significance when examining the maximum or average peak force and the area under the curve of the grip profiles. The analysis using simple regression in GraphPad Prism for fatigue (or force rundown) revealed a statistically significant effect in the morning data sets only, where, on exam days, fatigue was accelerated (slopes differed, p = 0.0013). The main statistically significant finding was an accelerated rate of hand grip fatigue in the morning but not on the afternoon exam days, suggesting that exams might be less stressful in the afternoon. Scheduling stressful exams in the afternoons might not affect muscle hand grip as much as exams in the morning and might correlate with better student academic outcomes. Health sciences/Health care Health sciences/Medical research Biological sciences/Psychology Social science/Psychology Medical School Summative Assessment Academic Stress Hand Grip Measurements Force Output Muscle Fatigue Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Medical school is widely regarded as one of the most demanding graduate education programs. In addition to mastering a vast amount of information within a compressed timeframe, students are frequently assessed through rigorous examinations designed to evaluate their knowledge and skills. These assessments are essential to ensure that graduates meet the national competency standards required for the Doctor of Medicine (MD) or Doctor of Osteopathic Medicine (DO) degrees. Exam performance not only contributes to academic progression but also plays a critical role in the selection process for residency placement and future career opportunities within a chosen specialty. While medical students often adapt to the regular cadence of examinations, these high-stakes assessments can be a significant source of stress, particularly when combined with secondary stressors such as financial pressure and the competitive academic environment. This cumulative stress has the potential to negatively affect both academic and physical performance, underscoring the need to understand better and mitigate its impact [ 1 ]. Previous research on the relationship between pre-exam stress and exam timing (morning vs. afternoon hours) is limited and difficult to find. Only a few studies have attempted to clarify the significance and correlation between stress levels, grip strength, and academic performance [ 2 ]. Earlier studies have shown that increased stress and cortisol levels can change performance in many professional or personal situations, whether educational [ 3 ], athletic [ 4 – 6 ], military [ 7 , 8 ], or age [ 9 ]. Most studies examined the relationship between stress levels, measured with cortisol levels [ 10 ] or not [ 11 ], with either academic performance (students’ grades) or athletic/professional performance [ 12 – 15 ]. Relating academic stress, particularly in medical school, to performance in terms of physical output or grip strength is an understudied area that requires further research. This study was initiated by a team of first-year medical students at the Idaho College of Osteopathic Medicine (ICOM) and funded through the ICOM Mentored Research Grant (MRG) program, which awards up to $ 3,000 to support student-led research in collaboration with a faculty mentor. The student team (the first three authors) developed the research concept and hypothesis in partnership with their faculty mentor (the last author) and successfully secured MRG funding through a competitive internal review process. While we originally intended to collect salivary cortisol samples at each handgrip testing session to assess physiological stress more directly, budget constraints made this infeasible, as the projected cost of cortisol assays across four sessions per participant exceeded available funds. Additionally, although we planned to correlate grip fatigue with exam performance, logistical and privacy restrictions prevented access to individual academic records, limiting our ability to explore this relationship within the scope of a student-led project. Despite the challenges and study’s limitations, we believed it was still possible to collect meaningful data that could offer valuable preliminary insights and lay the groundwork for future research, either by our team or others. With this in mind, we aimed to investigate whether elevated stress levels on exam days were associated with increased rates of muscle fatigue, which has been shown to correlate negatively with academic performance [ 2 , 16 ]. Previous studies suggest that excessive stress and elevated baseline cortisol levels can contribute to overall muscle fatigue and reductions in muscle strength and mass [ 17 ]. Given the well-documented morning surge in cortisol release, regulated by the hypothalamic-pituitary-adrenal (HPA) axis [ 18 ], we hypothesized that the timing of examinations (morning vs. afternoon) might influence baseline stress levels (see Fig. 1 for the theoretical framework). Specifically, we proposed that students who take their exams in the afternoon, when cortisol levels are typically lower [ 19 ], would exhibit stronger hand grip output, reduced muscle fatigue, and have potentially better exam outcomes. Although prior research has reported sex-based differences in cortisol responses [ 20 , 21 ], our study did not reveal particularly notable, statistically significant sex-based differences in the measured outcomes. Therefore, sex-specific results are not included in the main text but are available in the supplemental data files, which contain all raw participant-level data. Our study aimed to investigate the relationship between the time of day (known to impact cortisol levels) and stressful (exam) and non-stressful (non-exam) days, as well as potential correlations with student hand grip strength and fatigue (factors that could correlate with cognitive and academic performance). The initial aim was to examine whether hand grip function would show higher maximal tension and lower fatiguability in the afternoon compared to morning exam days. This will help determine if switching to afternoon examinations would benefit student academic performance more than those held in the morning. The objective was to explore this topic comprehensively, examining how stress levels impact students holistically. We sought to assess whether higher stress levels (specifically on exam days) reduced physical performance and increased muscle fatigue. To achieve this, we compared students’ hand grip strength and fatigue rates on exam days, when stress levels were elevated, with those on non-exam days, when stress levels were comparatively low. The initial three hypotheses were as follows: 1) Students' maximal grip strength and sustained force would decrease on stressful examination days compared to non-examination days, and 2) Students’ maximal grip strength and sustained force would decrease on a stressful morning examination day compared to a stressful afternoon examination day, 3 ) the potentially significant differences if our first two work hypothesis were proven to correct collected data could be used to make curriculum adjustments to shift the timing of examinations to be predominantly in the afternoon hours. 2. Methods and Materials 2.1 IRB Approval : All experimental protocols and the study design were reviewed and approved by the Boise State University IRB Committee (BSU IRB Protocol #998-MED22-007) to ensure the protection of the rights and welfare of human subjects. All data were reported as mean ± SEM or SD without individual identifiers and cannot be traced back to specific participants. Data files, recordings, analysis sheets, and student surveys were encrypted and password-protected both at the file level and on the researcher's laptop. All identifying information was removed during analysis and replaced with generic labels (e.g., "Student 1," "Student 2"). Only two documents, the original survey and the identification key, contained participant names, and both were securely stored and encrypted. 2.2 Student Recruitment : First-year medical students at the Idaho College of Osteopathic Medicine were recruited through multiple methods, including email outreach and in-class announcements. Recruitment emails were sent on several occasions, and a brief presentation was delivered at the start of a musculoskeletal (MSK) course lecture. This presentation explained the study, demonstrated the hand dynamometer equipment, and provided an opportunity for students to ask questions. No financial incentives or reimbursements were offered to students who volunteered, consented, and enrolled in the study. 2.3 Initial Student Information Collection : All methods adhered to federal and state research guidelines and regulations in the United States. After recruitment, students who agreed to participate were asked to complete a brief survey collecting demographic and background information, including name, medical school year, age, sex, weight, and frequency of weekly physical activity (defined as exercising for 30 minutes or more). Participants were informed that they could leave any question blank if they felt uncomfortable responding. A Google Sheets document was then distributed, allowing interested students to sign up for their preferred time slots to complete hand grip dynamometer testing. Each participant provided grip strength measurements on four distinct days: morning on an exam day, morning on a non-exam day, afternoon on an exam day, and afternoon on a non-exam day. Table 1 summarizes the demographic characteristics of the participants. 2.4 Data Collection : The first three authors of this study conducted the data collection. All participants were first-year medical students enrolled at the Idaho College of Osteopathic Medicine (ICOM), and each provided informed consent before participating. Students were instructed to squeeze a Vernier Go Direct® Hand Dynamometer with their dominant hand, exerting maximum effort for 30 seconds while trying to maintain maximum grip throughout. Each testing day included three 30-second trials with 10–15 seconds of rest between trials (see Fig. 2 for details). Participants who completed the full study contributed a total of 12 grip strength recordings across four sessions. Day 1 involved an afternoon session following an Anatomy practical exam. Day 2 was conducted in the morning before the Musculoskeletal system course final exam. Day 3 took place on a non-exam morning, representing a lower-stress condition. Day 4 was similar to Day 3 but occurred in the afternoon. This design enabled comparisons based on both time of day (morning vs. afternoon) and exam-related stress (exam vs. non-exam conditions). 2.5 Data Collection : The de-identified raw data records for each participant, including hand grip measurements, are provided in the Supplementary Information section as an Excel file titled 'ICOM_Student_Data_Complete.xlsx' 2.6 Data Analysis : Hand grip strength data were collected using a Vernier Go Direct® Hand Dynamometer connected to the Vernier Graphical Analysis Pro application on the researcher's iPad. Two types of data outputs were generated via the program: (1) graphical plots of Force (N) versus Time (s) and (2) time-series spreadsheets recording force values every 0.1 seconds for a 30-second trial. These grip force profiles were used to determine maximal grip tension and to create bar graphs comparing maximum force values (in Newtons) across different testing days (see Fig. 2 ). Table 1 Demographic data for this study include sex, age in years, weight in kgs, and the number of days students completed 30 minutes of exercise in a 7-day week. * One student didn’t provide weight. Characteristic N Mean Range Sex 21 • Female 8 • Male 13 Age, years 21 25.9 22–35 • Female 8 27.25 23–35 • Male 13 25.15 22–30 Weight, (kilograms) 20* 75.64 49.9-106.59 • Female 8 136.63 110–200 • Male 12* 186.83 145–235 30 mins of exercise per day per 7 days (days) 21 3.43 1–7 • Female 8 3.63 1–5 • Male 13 3.31 1–7 Initial data compilation was conducted in Microsoft Excel, where student data were organized into master spreadsheets corresponding to different calculated parameters, including mean (average) force, maximum force, slope of fatigue (force decline over time), and percent change. These calculations were performed separately for each testing day for each participant. Statistical analysis was performed using a combination of Microsoft Excel, GraphPad Prism 10, and R statistical software. For group comparisons of average maximal force, data were imported into GraphPad Prism and analyzed using ordinary one-way ANOVA, assuming a normal (Gaussian) distribution. Bartlett's test was used to assess the homogeneity of variances across groups. Bar graphs with error bars were generated to visualize group differences and trends. Maximum grip force was further analyzed and presented in Fig. 3 (A–D). Two approaches were used for statistical comparisons: (1) the highest single force value recorded from any of the three trials on a given day, and (2) the average of the three maximum force values for that day. These analyses were conducted separately for the 12 students who completed all four days of testing (Fig. 3 A–B) and for the full cohort of 21 students (Fig. 3 C–D). Additional subgroup analyses were conducted based on sex, comparing male-only and female-only groups among both the 12-completer and full 21-participant datasets. All subgroup comparisons were performed using GraphPad Prism’s ANOVA feature, and corresponding bar graphs with error bars were generated to illustrate the results. We used GraphPad Prism combined statistical approaches to calculate the differences in fatigue (or slopes of diminishing muscle tension), fitting simple linear regression lines from the 2-second to 22-second (20s period) of each averaged hand grip profile from the 21-student cohort. GraphPad Prism statistically analyzed if the slopes of the fitted simple regression lines (Fig. 4 ) differed for each pair of conditions (n = 21, assuming that each replicate plotted and placed on the Y axis had an individual point and was not a mean value). Prism compared slopes first and calculated a P value (two-tailed) testing the null hypothesis that the slopes are all identical (the lines are parallel). The P values (listed in Fig. 4 ) answered this question: if the slopes were identical, what is the chance that randomly selected data points would have slopes as different (or more different) than was observed in the study? This method is equivalent to an Analysis of Covariance (ANCOVA), although ANCOVA can be extended to more complicated situations. Further analysis was completed using a linear mixed model with R statistical software (findings are summarized in Fig. 5). First, the data were cleaned of outliers by removing students’ data sets in situations when they released or did not maintain continuous tension in their grip for the entire 30 seconds of each measurement. This showed only one student who released their grip during their 30-second time window. The linear mixed model in R was created at ICOM and Boise State University as a method where the student was the random effect, and there were four responses. Responses included 1) slope, 2) area under the curve, 3 ) maximum, and 4) average force. Each response was averaged over the three trials for a given day. Within the model, Morning vs Afternoon and Stressful vs non-stressful were examined. Slopes were calculated for each student starting from the 5th second to the 25th second of each 30-second-long trial (only fitting regression lines in the middle 20 seconds of each record). 3. Results The results were collected at the Idaho College of Osteopathic Medicine (ICOM) and analyzed collaboratively across three institutions: ICOM, Boise State University (BSU), and Sam Houston State University College of Osteopathic Medicine (SHSU-COM). Initial data collection and preliminary analysis were conducted at ICOM using GraphPad Prism software. Additional statistical analysis was performed using R at BSU, and the final validation of statistical outputs, as well as the creation of all figures and tables, were completed at SHSU-COM, where the manuscript was also finalized for publication. During the planning phase at the Idaho College of Osteopathic Medicine (ICOM), an initial power analysis determined that a minimum of 68 first-year medical students (approximately 50% of the entire class) would be required to achieve 80% statistical power. This estimate was based on an anticipated 15% difference in hand grip slope and maximum force values between experimental conditions (e.g., morning vs. afternoon or stressed vs. non-stressed). The sample size was conservatively calculated to ensure sufficient power to detect statistically meaningful differences between groups. Despite dedicated recruitment efforts, a total of 21 students enrolled in the study, contributing 207 individual hand grip recordings. Among participants, 38.1% identified as female and 61.9% as male. However, only 12 students, approximately 30% of the target sample size, completed all four experimental conditions and provided full data sets. While the final sample size limited the statistical power and generalizability of the findings, the observed trends offer valuable preliminary insights and identify directions for future research with larger, more representative cohorts. Initial data analysis using GraphPad Prism focused on the maximum force exerted by participants on the hand grip dynamometer. Maximum grip output was defined as the peak newton (N) value recorded during the first few seconds of each 30-second trial. For each student, the three highest values recorded on a given day were used to calculate both the absolute maximum (Figs. 3 A and 3 C) and the average maximal grip force (Figs. 3 B and 3 D). As shown in Fig. 3 , neither method of analysis revealed statistically significant differences in maximal hand grip strength. Two-way ANOVA testing indicated that neither time of day (morning vs. afternoon) nor stress condition (exam vs. non-exam) was a statistically significant factor in explaining variance in either absolute or average maximal grip force values (p > 0.05). Given the limited number of statistically significant findings in the initial analysis, we expanded our approach by employing additional statistical techniques, including a linear mixed model (LMM) using R statistical software. This analysis focused on fatigue rate, specifically calculating the area under the curve (AUC) of each trial’s force output over time. These additional computations were performed at Boise State University, with data from ICOM, and revealed no statistically significant differences across conditions (see Table 2 and Fig. 4 ). Table 2 Linear Mixed Model Results comparing Stress vs non-stress p-values and Morning vs Afternoon. Data analysis was completed using R Statistical software in BSU (n = 12–21, individual p-values were all p > 0.05). Responses Stress vs. Non-stress (p-values) Morning vs. Afternoon (p-values) Slope of fatigue 0.3425 0.8632 Area Under the Curve (ACU) 0.2150 0.4154 Maximum (Max) Force 0.8179 0.6456 Average (AVG) Force 0.6713 0.9982 Figure 4 presents four boxplots illustrating the response variables analyzed using the linear mixed model. Each boxplot displays the interquartile range (Q1–Q3), with a line indicating the median. Whiskers extend to the minimum and maximum values, and individual dots indicate outliers. Colors represent the day and condition of data collection: Exam Day – Afternoon (gray), Exam Day – Morning (yellow), Non-Exam Day – Morning (green), and Non-Exam Day – Afternoon (orange). No significant differences were detected between the four groups, as confirmed by statistical testing. Additionally, all outliers were manually reviewed and confirmed to be valid data points rather than entry or measurement errors. Discussion One of the biggest concerns for medical students isn't necessarily matching into their desired residency or ensuring long-term professional success but rather the fear of failing exams during the first two years of medical school. Even getting a minimal passing score can cause stress, as many students try to stay competitive by earning high marks, grades, or GPAs. This intense academic pressure leads to significant mental stress, which can negatively affect physical, cognitive, and academic performance. This study aimed to explore mind-body-spirit connections during exam-day stress and to see if hand grip strength could serve as an indirect stress marker and predictor of exam results. Previous research has shown that increased muscle fatigue is linked to declines in cognitive function and poorer academic outcomes. Our initial hypothesis was that grip strength performance would vary significantly between exam and non-exam days, partly due to changes in circulating cortisol levels, which are naturally higher in the morning and lower in the afternoon. Previous studies have shown that extreme stress can elevate cortisol levels, potentially impairing performance [ 26 ]. Even under non-stressful conditions, humans exhibit a natural, dynamic rise in plasma cortisol concentrations known as the cortisol awakening response (CAR) [ 27 ]. This response plays a vital role in mobilizing energy for the transition from sleep to wakefulness by releasing glucose to meet the demands of daily activities [ 28 ]. Beyond this normal morning surge, prolonged or chronic stress can lead to persistently elevated cortisol levels, which may have numerous adverse health consequences. These include increased weight gain and metabolic disturbances [ 29 ], high blood pressure [ 29 ], impaired immune function [ 30 ], digestive system issues [ 31 , 32 ], altered muscle tension [ 33 ], disrupted mindfulness [ 34 ], elevated risk of heart disease [ 35 ], weakened bones [ 36 , 37 ], mood swings [ 38 ], and poor sleep quality [ 39 , 40 ]. Unfortunately, a key limitation of our study was the lack of funding to collect and analyze salivary cortisol samples from each participant, which prevented us from directly examining the relationship between stress and cortisol levels on non-stress days. As an alternative, we used hand grip strength measurements as an indirect indicator of stress and potential fluctuations in cortisol levels. Although none of the analyses of maximal or averaged hand grip tension revealed statistically significant differences (Fig. 3 A-D), the scatter plots from the GraphPad Prism analysis (Fig. 3 A-D) showed some interesting trends for discussion or interpretation: 1) The mean-averaged trial records from the 21 morning exam sessions were higher (Fig. 3 A) than those from the non-exam morning sessions. However, this difference did not reach statistical significance due to variability and the small sample size. However, the slopes representing muscle tension decline (fatigue) were statistically significantly different between the two conditions. These results suggest that naturally elevated cortisol levels in the morning, when combined with additional cortisol released in response to exam-related stress, may enhance initial force production by motor units in the forearm. However, this heightened activation appears to be less sustainable over time, leading to accelerated fatigue. 2) A somewhat similar trend is observed in Fig. 3 D, where we compared the same 21 participants’ mean-averaged trial records from the morning exam session with those from the afternoon exam session. The predicted naturally elevated cortisol levels in the morning likely contributed to increased initial grip tension, although this difference did not reach statistical significance. The average tension was higher in the morning, and the rate of fatigue was similar between sessions, as indicated by non-significant differences in slope (p = 0.4263). Of our three initial hypotheses: (1) that students’ maximal grip strength and sustained force would decrease on stressful examination days compared to non-examination days was not supported by our findings; (2) that students’ maximal grip strength and sustained force would decrease on stressful morning examination days compared to stressful afternoon examination days was partially supported (specifically, we observed a significantly steeper fatigue slope in the morning sessions, indicating accelerated fatigue); and (3) the observed differences in our second hypothesis provide preliminary support for the idea that shifting examinations to the afternoon may reduce student stress and potentially improve exam performance. However, our confidence in this recommendation is limited due to some constraints and limitations encountered during the study’s design and data collection processes (see Limitations section). As shown in Fig. 3 , cortisol levels present in the morning appear to enhance the muscle’s ability to generate tension. However, this trend did not reach statistical significance, likely due to the small sample size. When examining the slope of the morning data, we observed that exam conditions significantly increased the rate of fatigue, a finding that was statistically significant only during the morning sessions. In contrast, no such effect was observed in the afternoon sessions. This suggests that administering exams in the afternoon may be less physiologically stressful, as muscle fatigue (calculated using our slope-based method in GraphPad Prism) was not significantly impacted by exam-related stress during that time of day. Limitations This study was designed to investigate whether acute academic stress affects physical performance, using hand-grip strength as a proxy measure. While we were unable to directly assess academic performance due to limitations in accessing student grades, grip strength served as a physiological indicator of how students' bodies responded to stress on exam versus non-exam days. By comparing morning and afternoon sessions, we aimed to determine whether exam timing influences fatigue and, by extension, academic readiness. Our findings suggest that morning exams may be associated with greater fatigue, possibly due to elevated stress levels. This raises the possibility that scheduling exams in the afternoon could reduce stress and support better overall performance, although further research is needed to confirm this hypothesis. Several limitations affected the outcomes of this study. An initial power analysis showed that at least 68 participants completing all four sessions were needed to achieve strong statistical significance. However, only 21 students enrolled, and just 12 completed all required data collections to form a complete dataset for each participant. A smaller sample size than what the power analysis projected reduces statistical power, making it harder to detect real effects and increasing the chance of Type II errors (false negatives). As a result, the study might not identify genuine differences or relationships, even if they exist in the population. This limited sample size likely decreased both the statistical power and the generalizability of the results. Additionally, budget restrictions prevented offering financial incentives, which could have negatively impacted participant recruitment. Potential sources of bias should also be acknowledged as limitations of this study. The Hawthorne effect may have led participants to exert greater effort during hand grip assessments due to the awareness of being observed. Additionally, stress levels could have been influenced by external academic pressures, such as simultaneous exams or assignments in other courses, as well as by personal life stressors. Measurement timing varied within a two-hour testing window, meaning some students were assessed closer to the exam start time than others, potentially affecting both stress levels and grip performance. Other uncontrolled variables, such as caffeine intake, sleep duration, and overall health status, may also have influenced the grip strength outcomes. Due to study constraints, our investigation focused solely on hand grip strength measurements using a dynamometer, without collecting data on academic performance or exam outcomes. Although this represents a limitation, previous research has identified correlations between muscle fatigue and cognitive decline, particularly in individuals with pre-existing cognitive impairments [ 22 , 23 ]. These findings support the rationale for investigating grip strength as a potential indirect indicator of academic readiness under stress. However, our study was limited by the lack of access to students' academic grades and physiological markers such as cortisol levels. Incorporating these measures prior to each grip strength assessment could have offered deeper insight into the interplay between stress, physical performance, and academic outcomes. Conclusion Following data collection at the Idaho College of Osteopathic Medicine and statistical analysis in collaboration with Boise State University and Sam Houston State University College of Osteopathic Medicine, we found no statistically significant differences in maximum or average hand grip strength between exam and non-exam days. However, a significant difference was observed in the slope of muscle fatigue, specifically in morning exam vs non-exam days, where the rate of decline in grip strength was increased when students had an exam as a stress factor. Additionally, these statistically significant changes become apparent when the regression analysis commences at the 2-second mark. This was not observed when using a 5-second starting point, emphasizing the impact of the analytical approach on observed outcomes. While most of our initial hypotheses were not supported, the finding of accelerated fatigue during morning exam sessions suggests that exam timing may influence physical manifestations of stress. As a student-led study conducted with limited resources, we acknowledge the constraints but also believe that this project makes a meaningful contribution to the field of medical education research. It provides a foundation for future work involving larger, more representative samples and more robust physiological and academic data. Given the global and ongoing challenge of academic stress in medical training, continued research is essential to better understand and mitigate its effects on student well-being and performance. Declarations Acknowledgments : This project was supported by the Idaho College of Osteopathic Medicine (ICOM) Research Department through the Mentored Research Grant (MRG) Program, which provided up to $3,000 for data acquisition and materials and up to $3,000 for student wages ($15/hour for up to 200 hours). Grant funds were used to purchase three Go Direct® Hand Dynamometers, three USB-C to USB Apple Adapters, and one departmental license of Vernier Graphical Analysis Pro. We thank Boise State University’s IRB for reviewing and approving the human subject protocol. We are also grateful to Dr. Jacob Kammer (ICOM) and Dr. Megan Null (Boise State University) for their support with R software analysis and statistical interpretation. The authors further acknowledge funding support from Sam Houston State University College of Osteopathic Medicine (SHSU-COM) for covering the Open Access publication fee, enabling this manuscript to be made freely available upon publication. Competing Interests: There were no competing or conflicts of interest for any of the authors of this study during or after the completion of this research CRediT author statement: Dominic Giandonato (DG): Conceptualization, Methodology, Investigation, Formal Analysis, Writing – Original Draft, Funding Acquisition. Nicholas Rincon (NR): Investigation. Nathan Adamietz (NA): Investigation. Mihail Mitov (MM): Conceptualization, Methodology, Formal Analysis, Writing – Original Draft, Visualization, Funding Acquisition, Supervision. 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Elshennawy, Physical and cognitive consequences of fatigue: A review. Journal of Advanced Research, 2015. 6 (3): p. 351-358. Kobilo, T. and H. Van Praag, Muscle Fatigue and Cognition: What is the Link? Frontiers in Physiology, 2012. 3 . Špiljak, B., et al. A Review of Psychological Stress among Students and Its Assessment Using Salivary Biomarkers . Behavioral Sciences, 2022. 12 , DOI: 10.3390/bs12100400. Law, R. and A. Clow, Chapter Eight - Stress, the cortisol awakening response and cognitive function , in International Review of Neurobiology , A. Clow and N. Smyth, Editors. 2020, Academic Press. p. 187-217. Chauhan, S., et al., Beyond sleep: A multidimensional model of chronotype. Neuroscience & Biobehavioral Reviews, 2023. 148 : p. 105114. Bini, J., et al., Stress-level glucocorticoids increase fasting hunger and decrease cerebral blood flow in regions regulating eating. NeuroImage: Clinical, 2022. 36 : p. 103202. Sharma, A., et al., Cortisol affects macrophage polarization by inducing miR-143/145 cluster to reprogram glucose metabolism and by promoting TCA cycle anaplerosis. Journal of Biological Chemistry, 2024. 300 (10): p. 107753. Rodiño-Janeiro, B.K., et al., Acute stress triggers sex-dependent rapid alterations in the human small intestine microbiota composition. Frontiers in Microbiology, 2025. 15 . Roca Rubio, M.F., et al., Associations between various markers of intestinal barrier and immune function after a high-intensity exercise challenge. Physiological Reports, 2024. 12 (10): p. e16087. Anderson, G.S., et al., The Impact of Acute Stress Physiology on Skilled Motor Performance: Implications for Policing. Frontiers in Psychology, 2019. 10 . Gallistl, M., et al., Evidence for differential associations of distinct trait mindfulness facets with acute and chronic stress. Psychoneuroendocrinology, 2024. 166 : p. 107051. Faresjö, Å., et al., Higher hair cortisol levels associated with previous cardiovascular events and cardiovascular risks in a large cross-sectional population study. BMC Cardiovascular Disorders, 2024. 24 (1): p. 536. Cvijetic, S., et al., Diurnal Salivary Cortisol in Relation to Body Composition and Heart Rate Variability in Young Adults. Frontiers in Endocrinology, 2022. 13 . Zhu, K., et al., Associations between hypothalamic–pituitary–adrenal axis function and peak bone mass at 20years of age in a birth cohort. Bone, 2016. 85 : p. 37-44. Dovom, M.M., et al., Effects of Official Chess Competition on Salivary Cortisol and Mood Swings in Adolescent Girls: A Win–Loss Approach. Applied Psychophysiology and Biofeedback, 2024. 49 (2): p. 301-311. Hannibal, K.E. and M.D. Bishop, Chronic Stress, Cortisol Dysfunction, and Pain: A Psychoneuroendocrine Rationale for Stress Management in Pain Rehabilitation. Physical Therapy, 2014. 94 (12): p. 1816-1825. Castillo-Navarrete, J.L., A. Guzmán-Castillo, and C. Bustos, Longitudinal analysis of academic stress and its effects on salivary cortisol, alpha-amylase, and academic outcomes: Study protocol. PLOS ONE, 2024. 19 (12): p. e0315650. Additional Declarations No competing interests reported. Supplementary Files ICOMStudentDataComplete.xlsx Cite Share Download PDF Status: Published Journal Publication published 07 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 08 Sep, 2025 Reviews received at journal 01 Sep, 2025 Reviews received at journal 28 Aug, 2025 Reviews received at journal 24 Aug, 2025 Reviewers agreed at journal 24 Aug, 2025 Reviews received at journal 23 Aug, 2025 Reviews received at journal 21 Aug, 2025 Reviewers agreed at journal 21 Aug, 2025 Reviewers agreed at journal 21 Aug, 2025 Reviews received at journal 20 Aug, 2025 Reviews received at journal 20 Aug, 2025 Reviewers agreed at journal 20 Aug, 2025 Reviews received at journal 20 Aug, 2025 Reviewers agreed at journal 20 Aug, 2025 Reviewers agreed at journal 19 Aug, 2025 Reviewers agreed at journal 19 Aug, 2025 Reviewers agreed at journal 19 Aug, 2025 Reviewers invited by journal 19 Aug, 2025 Editor assigned by journal 19 Aug, 2025 Editor invited by journal 22 Jul, 2025 Submission checks completed at journal 16 Jul, 2025 First submitted to journal 16 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6985107","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":504769434,"identity":"52f03893-345a-49e3-8b1b-97f03679b7dd","order_by":0,"name":"Dominic Giandonato","email":"","orcid":"","institution":"Idaho College of Osteopathic Medicine","correspondingAuthor":false,"prefix":"","firstName":"Dominic","middleName":"","lastName":"Giandonato","suffix":""},{"id":504769435,"identity":"f875fa19-912d-4f54-8986-5f9de966533e","order_by":1,"name":"Nicholas Rincon","email":"","orcid":"","institution":"Idaho College of Osteopathic Medicine","correspondingAuthor":false,"prefix":"","firstName":"Nicholas","middleName":"","lastName":"Rincon","suffix":""},{"id":504769436,"identity":"0d75e3ee-4bd1-4859-92f0-65ef754c02ac","order_by":2,"name":"Nathan Adamietz","email":"","orcid":"","institution":"Idaho College of Osteopathic Medicine","correspondingAuthor":false,"prefix":"","firstName":"Nathan","middleName":"","lastName":"Adamietz","suffix":""},{"id":504769437,"identity":"bfe63253-4352-46a6-8964-b87bb954ebf5","order_by":3,"name":"Mihail Mitov","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAApElEQVRIiWNgGAWjYFACxgYgYcNgAOawEaelEagnjSQtYGsOk6BFd9rh9gcf287bm/OfMWD4UHaYsBaz24mNjTPbbifunJFjwDjjHJFamnnO3E4wuMFjwMzbRryWc/YG588YMP8lXkvFAcYNB3IMmBmJ1TJzRkVy4oYbaQUHe86lE6Ml/cGHDwZ2QIcd3vjgR5k1YS0o4ACJ6kfBKBgFo2AU4AIAwYdAiTY3mSgAAAAASUVORK5CYII=","orcid":"","institution":"Sam Houston State University College of Osteopathic Medicine","correspondingAuthor":true,"prefix":"","firstName":"Mihail","middleName":"","lastName":"Mitov","suffix":""}],"badges":[],"createdAt":"2025-06-26 15:53:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6985107/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6985107/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-33886-8","type":"published","date":"2026-01-07T15:57:11+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":90299197,"identity":"5ac599cc-0eff-4ff2-8da5-2dfaf38f432e","added_by":"auto","created_at":"2025-09-01 08:49:55","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":102637,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eThis study is grounded in the theoretical framework of circadian cortisol variation and its influence on physiological performance under stress\u003c/em\u003e: Cortisol levels in humans follow a well-established diurnal rhythm, typically peaking in the early morning and gradually declining throughout the day. Among medical students, this natural pattern is likely preserved during non-exam (baseline) days. However, we hypothesize that on high-stress days, such as examination days, basal cortisol levels may be elevated—particularly in the morning—resulting in a measurable delta (Δ) between exam and non-exam conditions in morning or afternoon exam conditions.\u003c/p\u003e\n\u003cp\u003eThis hypothetical Δ represents the increase in cortisol attributable to acute stress. While this concept is central to our model linking stress to muscle fatigue, we were unable to directly quantify cortisol levels due to budgetary constraints that precluded salivary sample collection and analysis. Moreover, individual variability in cortisol response could be influenced by confounding factors such as chronic stress, sleep disturbances, diet, caffeine or alcohol intake, infections, medications, and other lifestyle or health-related variables.\u003c/p\u003e\n\u003cp\u003eFuture studies should aim to incorporate direct cortisol measurements and control for these confounding factors to more precisely characterize the relationship between physiological stress markers and muscle fatigue. Such refinements will strengthen the utility of hand grip analysis as a non-invasive proxy for stress-related performance changes in academic settings.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6985107/v1/439eb2eb05d465b6d215da21.jpg"},{"id":90301264,"identity":"77e988e6-f75c-4b8a-9028-0d8f6a66d7e5","added_by":"auto","created_at":"2025-09-01 08:57:55","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":84395,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eIllustration of experimental data collection and force peak analysis algorithm:\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e Courtesy image from Vernier, depicting the Go Direct® Hand Dynamometer used for data collection in this study. This device enabled real-time measurement of hand grip force output during 30-second maximal contraction trials.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(B)\u003c/strong\u003e Sample dataset from a single student recorded on an afternoon exam day, showing three consecutive hand grip trials. This panel illustrates the algorithm used to extract the maximum and average force values for each trial. Data were processed using Vernier Graphical Analysis Pro software, and statistical metrics such as peak force (maximum tension) and average force across the contraction period were computed for each recording.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6985107/v1/e6dbb70cebef896f694f76dd.jpg"},{"id":90299199,"identity":"50c300a8-22e7-416b-874d-2fa98d49892a","added_by":"auto","created_at":"2025-09-01 08:49:55","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":129432,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eBar graphs of maximal hand grip force, including different subsets of students who participated in the study. This graph displays the statistical comparison between the student’s hand force calculated in 4 different ways\u003c/em\u003e: \u003cstrong\u003eA)\u003c/strong\u003e only the 12 students who completed the 4 data point measurements are included, and the single highest maximum point of 3 hand grips trial measurements is taken in this comparison (n=12, time of the day p=0.9817, stress/exam levels p=0.5634, 2-way ANOVA, GraphPad Prism); \u003cstrong\u003eB)\u003c/strong\u003e only the 12 students who completed the 4 data point measurements are included, and an average of the highest maximum point of 3 hand grips trial measurements is taken in this comparison (n=12, time of the day p=0.9914, stress/exam levels p=0.8812, 2-way ANOVA, GraphPad Prism); \u003cstrong\u003eC)\u003c/strong\u003e all 21 students are included, and the single highest maximum point of 3 hand grips trial measurements is taken in this comparison (n=21, time of the day p=0.9948, stress/exam levels p=0.3130, 2-way ANOVA, GraphPad Prism) and \u003cstrong\u003eD)\u003c/strong\u003e all 21 students are included, and an average of the highest maximum point of 3 hand grips trial measurements is taken in this comparison n (n=21, time of the day p=0.9752, stress/exam levels p=0.6785, 2-way ANOVA, GraphPad Prism). The y-axis displays the single maximum (Max) or the average of 3 trials (Avg) in Newtons, and the x-axis displays the time of day and stress level of the day. Each circle and triangle represents an individual data point plotted.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6985107/v1/dc77ecdf7ef7d931b1962bf2.jpg"},{"id":90299233,"identity":"b08aed65-dc23-432f-9322-f8ad64a7f8e1","added_by":"auto","created_at":"2025-09-01 08:49:55","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":165152,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eScatter plots display data from all 21 students who completed the study.\u003c/em\u003e \u003cstrong\u003ePanel (A)\u003c/strong\u003e compares fatigue slopes between No Exam Morning (Blue) and Exam Morning (Orange); \u003cstrong\u003e(B)\u003c/strong\u003e compares No Exam Afternoon (Green) with Exam Afternoon (Red); \u003cstrong\u003e(C)\u003c/strong\u003e compares No Exam Morning (Blue) with No Exam Afternoon (Green); \u003cstrong\u003e(D)\u003c/strong\u003e compares Exam Morning (Orange) with Exam Afternoon (Red).\u003c/p\u003e\n\u003cp\u003eSlopes were calculated by fitting simple linear regression lines to force data between 2 and 22 seconds (a 20-second window) of each participant’s averaged hand grip profile. GraphPad Prism was used to statistically compare slopes between conditions (n = 21), treating each individual slope as a single data point. Two-tailed P values were computed to test the null hypothesis that the slopes are identical (i.e., lines are parallel). The P values in each panel indicate the probability of observing the measured slope differences by chance. This analysis mirrors an Analysis of Covariance (ANCOVA) and includes an F-value, representing the ratio of between-group to within-group variability, with higher F-values indicating a stronger group effect.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6985107/v1/66d5f7219a7233136f28d647.jpg"},{"id":90299203,"identity":"cf86a220-441b-40cf-a969-83540e35e77f","added_by":"auto","created_at":"2025-09-01 08:49:55","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":83929,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eStatistical Analysis of Hand Grip Force Metrics Using R (n = 12–21): \u003c/em\u003eData analysis was conducted using a linear mixed-effects model in R (version X.X.X) with support from the Boise State University statistical consulting core. Four key outcome measures were evaluated across experimental conditions: \u003cstrong\u003e(A) \u003c/strong\u003eBox plot of the fatigue slope, calculated from the middle 20 seconds (excluding the first and last 5 seconds of each 30-second tension record); \u003cstrong\u003e(B)\u003c/strong\u003e Box plot of the area under the curve (AUC) from 0 to 30 seconds, displayed on the y-axis in Newtons²;\u003cem\u003e \u003c/em\u003e\u003cstrong\u003e(C)\u003c/strong\u003e Box plot of the maximum force (peak tension) in Newtons;\u003cem\u003e \u003c/em\u003e\u003cstrong\u003e(D)\u003c/strong\u003e Box plot of the averaged maximal force across three grip trials per condition.\u003cem\u003e \u003c/em\u003eThe four experimental conditions compared in each plot are color-coded as follows:\u003cem\u003e \u003c/em\u003eNo Exam Morning (Blue)\u003cem\u003e; \u003c/em\u003eNo Exam Afternoon (Green)\u003cem\u003e; \u003c/em\u003eExam Morning (Yellow), and\u003cem\u003e \u003c/em\u003eExam Afternoon (Orange).\u003cem\u003e \u003c/em\u003eThis analysis enabled within-subject comparisons while accounting for repeated measures and participant variability.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6985107/v1/86e04e1e06c218d2b16eda24.jpg"},{"id":100069510,"identity":"8770caf2-9d7c-47f6-8d5f-5f20fbd425fa","added_by":"auto","created_at":"2026-01-12 16:14:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1281917,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6985107/v1/9915c6ef-63a4-4f6b-9cff-eab5d5650e1b.pdf"},{"id":90299202,"identity":"7ae15efb-5c1a-4001-9c70-1688ed085af8","added_by":"auto","created_at":"2025-09-01 08:49:55","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1367588,"visible":true,"origin":"","legend":"","description":"","filename":"ICOMStudentDataComplete.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6985107/v1/4795280f5e86f2bcfe48f07d.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impact of Exam Stress and Time of Day on Hand Grip Fatigue in Medical Students","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMedical school is widely regarded as one of the most demanding graduate education programs. In addition to mastering a vast amount of information within a compressed timeframe, students are frequently assessed through rigorous examinations designed to evaluate their knowledge and skills. These assessments are essential to ensure that graduates meet the national competency standards required for the Doctor of Medicine (MD) or Doctor of Osteopathic Medicine (DO) degrees. Exam performance not only contributes to academic progression but also plays a critical role in the selection process for residency placement and future career opportunities within a chosen specialty.\u003c/p\u003e\u003cp\u003eWhile medical students often adapt to the regular cadence of examinations, these high-stakes assessments can be a significant source of stress, particularly when combined with secondary stressors such as financial pressure and the competitive academic environment. This cumulative stress has the potential to negatively affect both academic and physical performance, underscoring the need to understand better and mitigate its impact [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e\u003cp\u003ePrevious research on the relationship between pre-exam stress and exam timing (morning vs. afternoon hours) is limited and difficult to find. Only a few studies have attempted to clarify the significance and correlation between stress levels, grip strength, and academic performance [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Earlier studies have shown that increased stress and cortisol levels can change performance in many professional or personal situations, whether educational [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], athletic [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], military [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], or age [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Most studies examined the relationship between stress levels, measured with cortisol levels [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] or not [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], with either academic performance (students\u0026rsquo; grades) or athletic/professional performance [\u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Relating academic stress, particularly in medical school, to performance in terms of physical output or grip strength is an understudied area that requires further research.\u003c/p\u003e\u003cp\u003eThis study was initiated by a team of first-year medical students at the Idaho College of Osteopathic Medicine (ICOM) and funded through the ICOM Mentored Research Grant (MRG) program, which awards up to \u003cspan\u003e$\u003c/span\u003e3,000 to support student-led research in collaboration with a faculty mentor. The student team (the first three authors) developed the research concept and hypothesis in partnership with their faculty mentor (the last author) and successfully secured MRG funding through a competitive internal review process.\u003c/p\u003e\u003cp\u003e While we originally intended to collect salivary cortisol samples at each handgrip testing session to assess physiological stress more directly, budget constraints made this infeasible, as the projected cost of cortisol assays across four sessions per participant exceeded available funds. Additionally, although we planned to correlate grip fatigue with exam performance, logistical and privacy restrictions prevented access to individual academic records, limiting our ability to explore this relationship within the scope of a student-led project.\u003c/p\u003e\u003cp\u003eDespite the challenges and study\u0026rsquo;s limitations, we believed it was still possible to collect meaningful data that could offer valuable preliminary insights and lay the groundwork for future research, either by our team or others. With this in mind, we aimed to investigate whether elevated stress levels on exam days were associated with increased rates of muscle fatigue, which has been shown to correlate negatively with academic performance [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Previous studies suggest that excessive stress and elevated baseline cortisol levels can contribute to overall muscle fatigue and reductions in muscle strength and mass [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eGiven the well-documented morning surge in cortisol release, regulated by the hypothalamic-pituitary-adrenal (HPA) axis [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], we hypothesized that the timing of examinations (morning vs. afternoon) might influence baseline stress levels (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e for the theoretical framework). Specifically, we proposed that students who take their exams in the afternoon, when cortisol levels are typically lower [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], would exhibit stronger hand grip output, reduced muscle fatigue, and have potentially better exam outcomes. Although prior research has reported sex-based differences in cortisol responses [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], our study did not reveal particularly notable, statistically significant sex-based differences in the measured outcomes. Therefore, sex-specific results are not included in the main text but are available in the supplemental data files, which contain all raw participant-level data.\u003c/p\u003e\u003cp\u003eOur study aimed to investigate the relationship between the time of day (known to impact cortisol levels) and stressful (exam) and non-stressful (non-exam) days, as well as potential correlations with student hand grip strength and fatigue (factors that could correlate with cognitive and academic performance). The initial aim was to examine whether hand grip function would show higher maximal tension and lower fatiguability in the afternoon compared to morning exam days. This will help determine if switching to afternoon examinations would benefit student academic performance more than those held in the morning. The objective was to explore this topic comprehensively, examining how stress levels impact students holistically. We sought to assess whether higher stress levels (specifically on exam days) reduced physical performance and increased muscle fatigue. To achieve this, we compared students\u0026rsquo; hand grip strength and fatigue rates on exam days, when stress levels were elevated, with those on non-exam days, when stress levels were comparatively low.\u003c/p\u003e\u003cp\u003eThe initial three hypotheses were as follows: \u003cb\u003e1)\u003c/b\u003e Students' maximal grip strength and sustained force would decrease on stressful examination days compared to non-examination days, and \u003cb\u003e2)\u003c/b\u003e Students\u0026rsquo; maximal grip strength and sustained force would decrease on a stressful morning examination day compared to a stressful afternoon examination day, \u003cb\u003e3\u003c/b\u003e\u003cem\u003e)\u003c/em\u003e the potentially significant differences if our first two work hypothesis were proven to correct collected data could be used to make curriculum adjustments to shift the timing of examinations to be predominantly in the afternoon hours.\u003c/p\u003e"},{"header":"2. Methods and Materials","content":"\u003cp\u003e\u003cb\u003e2.1 IRB Approval\u003c/b\u003e: All experimental protocols and the study design were reviewed and approved by the Boise State University IRB Committee (BSU IRB Protocol #998-MED22-007) to ensure the protection of the rights and welfare of human subjects. All data were reported as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM or SD without individual identifiers and cannot be traced back to specific participants. Data files, recordings, analysis sheets, and student surveys were encrypted and password-protected both at the file level and on the researcher's laptop. All identifying information was removed during analysis and replaced with generic labels (e.g., \"Student 1,\" \"Student 2\"). Only two documents, the original survey and the identification key, contained participant names, and both were securely stored and encrypted.\u003c/p\u003e\u003cp\u003e\u003cb\u003e2.2 Student Recruitment\u003c/b\u003e: First-year medical students at the Idaho College of Osteopathic Medicine were recruited through multiple methods, including email outreach and in-class announcements. Recruitment emails were sent on several occasions, and a brief presentation was delivered at the start of a musculoskeletal (MSK) course lecture. This presentation explained the study, demonstrated the hand dynamometer equipment, and provided an opportunity for students to ask questions. No financial incentives or reimbursements were offered to students who volunteered, consented, and enrolled in the study.\u003c/p\u003e\u003cp\u003e\u003cb\u003e2.3 Initial Student Information Collection\u003c/b\u003e: All methods adhered to federal and state research guidelines and regulations in the United States. After recruitment, students who agreed to participate were asked to complete a brief survey collecting demographic and background information, including name, medical school year, age, sex, weight, and frequency of weekly physical activity (defined as exercising for 30 minutes or more). Participants were informed that they could leave any question blank if they felt uncomfortable responding. A Google Sheets document was then distributed, allowing interested students to sign up for their preferred time slots to complete hand grip dynamometer testing. Each participant provided grip strength measurements on four distinct days: morning on an exam day, morning on a non-exam day, afternoon on an exam day, and afternoon on a non-exam day. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the demographic characteristics of the participants.\u003c/p\u003e\u003cp\u003e\u003cb\u003e2.4 Data Collection\u003c/b\u003e: The first three authors of this study conducted the data collection. All participants were first-year medical students enrolled at the Idaho College of Osteopathic Medicine (ICOM), and each provided informed consent before participating. Students were instructed to squeeze a Vernier Go Direct\u0026reg; Hand Dynamometer with their dominant hand, exerting maximum effort for 30 seconds while trying to maintain maximum grip throughout. Each testing day included three 30-second trials with 10\u0026ndash;15 seconds of rest between trials (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e for details). Participants who completed the full study contributed a total of 12 grip strength recordings across four sessions. Day 1 involved an afternoon session following an Anatomy practical exam. Day 2 was conducted in the morning before the Musculoskeletal system course final exam. Day 3 took place on a non-exam morning, representing a lower-stress condition. Day 4 was similar to Day 3 but occurred in the afternoon. This design enabled comparisons based on both time of day (morning vs. afternoon) and exam-related stress (exam vs. non-exam conditions).\u003c/p\u003e\u003cp\u003e\u003cb\u003e2.5 Data Collection\u003c/b\u003e: The de-identified raw data records for each participant, including hand grip measurements, are provided in the Supplementary Information section as an Excel file titled 'ICOM_Student_Data_Complete.xlsx'\u003c/p\u003e\u003cp\u003e\u003cb\u003e2.6 Data Analysis\u003c/b\u003e: Hand grip strength data were collected using a Vernier Go Direct\u0026reg; Hand Dynamometer connected to the Vernier Graphical Analysis Pro application on the researcher's iPad. Two types of data outputs were generated via the program: (1) graphical plots of Force (N) versus Time (s) and (2) time-series spreadsheets recording force values every 0.1 seconds for a 30-second trial. These grip force profiles were used to determine maximal grip tension and to create bar graphs comparing maximum force values (in Newtons) across different testing days (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDemographic data for this study include sex, age in years, weight in kgs, and the number of days students completed 30 minutes of exercise in a 7-day week. * One student didn\u0026rsquo;t provide weight.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCharacteristic\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eMean\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eRange\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSex\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026bull; Female\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026bull; Male\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAge, years\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e25.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22\u0026ndash;35\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026bull; Female\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e27.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23\u0026ndash;35\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026bull; Male\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e25.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22\u0026ndash;30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eWeight, (kilograms)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e75.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e49.9-106.59\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026bull; Female\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e136.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e110\u0026ndash;200\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026bull; Male\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e186.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e145\u0026ndash;235\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e30 mins of exercise per day per 7 days (days)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u0026ndash;7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026bull; Female\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u0026ndash;5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026bull; Male\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u0026ndash;7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eInitial data compilation was conducted in Microsoft Excel, where student data were organized into master spreadsheets corresponding to different calculated parameters, including mean (average) force, maximum force, slope of fatigue (force decline over time), and percent change. These calculations were performed separately for each testing day for each participant.\u003c/p\u003e\u003cp\u003eStatistical analysis was performed using a combination of Microsoft Excel, GraphPad Prism 10, and R statistical software. For group comparisons of average maximal force, data were imported into GraphPad Prism and analyzed using ordinary one-way ANOVA, assuming a normal (Gaussian) distribution. Bartlett's test was used to assess the homogeneity of variances across groups. Bar graphs with error bars were generated to visualize group differences and trends.\u003c/p\u003e\u003cp\u003eMaximum grip force was further analyzed and presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e (A\u0026ndash;D). Two approaches were used for statistical comparisons: (1) the highest single force value recorded from any of the three trials on a given day, and (2) the average of the three maximum force values for that day. These analyses were conducted separately for the 12 students who completed all four days of testing (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA\u0026ndash;B) and for the full cohort of 21 students (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC\u0026ndash;D). Additional subgroup analyses were conducted based on sex, comparing male-only and female-only groups among both the 12-completer and full 21-participant datasets.\u003c/p\u003e\u003cp\u003eAll subgroup comparisons were performed using GraphPad Prism\u0026rsquo;s ANOVA feature, and corresponding bar graphs with error bars were generated to illustrate the results. We used GraphPad Prism combined statistical approaches to calculate the differences in fatigue (or slopes of diminishing muscle tension), fitting simple linear regression lines from the 2-second to 22-second (20s period) of each averaged hand grip profile from the 21-student cohort. GraphPad Prism statistically analyzed if the slopes of the fitted simple regression lines (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e) differed for each pair of conditions (n\u0026thinsp;=\u0026thinsp;21, assuming that each replicate plotted and placed on the Y axis had an individual point and was not a mean value). Prism compared slopes first and calculated a P value (two-tailed) testing the null hypothesis that the slopes are all identical (the lines are parallel). The P values (listed in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e) answered this question: if the slopes were identical, what is the chance that randomly selected data points would have slopes as different (or more different) than was observed in the study? This method is equivalent to an Analysis of Covariance (ANCOVA), although ANCOVA can be extended to more complicated situations.\u003c/p\u003e\u003cp\u003eFurther analysis was completed using a linear mixed model with R statistical software (findings are summarized in Fig.\u0026nbsp;5). First, the data were cleaned of outliers by removing students\u0026rsquo; data sets in situations when they released or did not maintain continuous tension in their grip for the entire 30 seconds of each measurement. This showed only one student who released their grip during their 30-second time window. The linear mixed model in R was created at ICOM and Boise State University as a method where the student was the random effect, and there were four responses. Responses included \u003cb\u003e1)\u003c/b\u003e slope, \u003cb\u003e2)\u003c/b\u003e area under the curve, \u003cb\u003e3\u003c/b\u003e\u003cb\u003e)\u003c/b\u003e maximum, and \u003cb\u003e4)\u003c/b\u003e average force. Each response was averaged over the three trials for a given day. Within the model, Morning vs Afternoon and Stressful vs non-stressful were examined. Slopes were calculated for each student starting from the 5th second to the 25th second of each 30-second-long trial (only fitting regression lines in the middle 20 seconds of each record).\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003eThe results were collected at the Idaho College of Osteopathic Medicine (ICOM) and analyzed collaboratively across three institutions: ICOM, Boise State University (BSU), and Sam Houston State University College of Osteopathic Medicine (SHSU-COM). Initial data collection and preliminary analysis were conducted at ICOM using GraphPad Prism software. Additional statistical analysis was performed using R at BSU, and the final validation of statistical outputs, as well as the creation of all figures and tables, were completed at SHSU-COM, where the manuscript was also finalized for publication.\u003c/p\u003e\u003cp\u003eDuring the planning phase at the Idaho College of Osteopathic Medicine (ICOM), an initial power analysis determined that a minimum of 68 first-year medical students (approximately 50% of the entire class) would be required to achieve 80% statistical power. This estimate was based on an anticipated 15% difference in hand grip slope and maximum force values between experimental conditions (e.g., morning vs. afternoon or stressed vs. non-stressed). The sample size was conservatively calculated to ensure sufficient power to detect statistically meaningful differences between groups.\u003c/p\u003e\u003cp\u003eDespite dedicated recruitment efforts, a total of 21 students enrolled in the study, contributing 207 individual hand grip recordings. Among participants, 38.1% identified as female and 61.9% as male. However, only 12 students, approximately 30% of the target sample size, completed all four experimental conditions and provided full data sets. While the final sample size limited the statistical power and generalizability of the findings, the observed trends offer valuable preliminary insights and identify directions for future research with larger, more representative cohorts.\u003c/p\u003e\u003cp\u003eInitial data analysis using GraphPad Prism focused on the maximum force exerted by participants on the hand grip dynamometer. Maximum grip output was defined as the peak newton (N) value recorded during the first few seconds of each 30-second trial. For each student, the three highest values recorded on a given day were used to calculate both the absolute maximum (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC) and the average maximal grip force (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, neither method of analysis revealed statistically significant differences in maximal hand grip strength. Two-way ANOVA testing indicated that neither time of day (morning vs. afternoon) nor stress condition (exam vs. non-exam) was a statistically significant factor in explaining variance in either absolute or average maximal grip force values (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003eGiven the limited number of statistically significant findings in the initial analysis, we expanded our approach by employing additional statistical techniques, including a linear mixed model (LMM) using R statistical software. This analysis focused on fatigue rate, specifically calculating the area under the curve (AUC) of each trial\u0026rsquo;s force output over time. These additional computations were performed at Boise State University, with data from ICOM, and revealed no statistically significant differences across conditions (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eLinear Mixed Model Results comparing Stress vs non-stress p-values and Morning vs Afternoon. Data analysis was completed using R Statistical software in BSU (n\u0026thinsp;=\u0026thinsp;12\u0026ndash;21, individual p-values were all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eResponses\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eStress vs. Non-stress\u003c/em\u003e\u003c/p\u003e\u003cp\u003e(p-values)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eMorning vs. Afternoon\u003c/em\u003e\u003c/p\u003e\u003cp\u003e(p-values)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSlope of fatigue\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.3425\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.8632\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eArea Under the Curve (ACU)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.2150\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.4154\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMaximum (Max) Force\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.8179\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.6456\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAverage (AVG) Force\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.6713\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.9982\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents four boxplots illustrating the response variables analyzed using the linear mixed model. Each boxplot displays the interquartile range (Q1\u0026ndash;Q3), with a line indicating the median. Whiskers extend to the minimum and maximum values, and individual dots indicate outliers. Colors represent the day and condition of data collection: Exam Day \u0026ndash; Afternoon (gray), Exam Day \u0026ndash; Morning (yellow), Non-Exam Day \u0026ndash; Morning (green), and Non-Exam Day \u0026ndash; Afternoon (orange). No significant differences were detected between the four groups, as confirmed by statistical testing. Additionally, all outliers were manually reviewed and confirmed to be valid data points rather than entry or measurement errors.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOne of the biggest concerns for medical students isn't necessarily matching into their desired residency or ensuring long-term professional success but rather the fear of failing exams during the first two years of medical school. Even getting a minimal passing score can cause stress, as many students try to stay competitive by earning high marks, grades, or GPAs. This intense academic pressure leads to significant mental stress, which can negatively affect physical, cognitive, and academic performance. This study aimed to explore mind-body-spirit connections during exam-day stress and to see if hand grip strength could serve as an indirect stress marker and predictor of exam results. Previous research has shown that increased muscle fatigue is linked to declines in cognitive function and poorer academic outcomes. Our initial hypothesis was that grip strength performance would vary significantly between exam and non-exam days, partly due to changes in circulating cortisol levels, which are naturally higher in the morning and lower in the afternoon. Previous studies have shown that extreme stress can elevate cortisol levels, potentially impairing performance [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Even under non-stressful conditions, humans exhibit a natural, dynamic rise in plasma cortisol concentrations known as the cortisol awakening response (CAR) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. This response plays a vital role in mobilizing energy for the transition from sleep to wakefulness by releasing glucose to meet the demands of daily activities [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Beyond this normal morning surge, prolonged or chronic stress can lead to persistently elevated cortisol levels, which may have numerous adverse health consequences. These include increased weight gain and metabolic disturbances [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], high blood pressure [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], impaired immune function [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], digestive system issues [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], altered muscle tension [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], disrupted mindfulness [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], elevated risk of heart disease [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], weakened bones [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], mood swings [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], and poor sleep quality [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Unfortunately, a key limitation of our study was the lack of funding to collect and analyze salivary cortisol samples from each participant, which prevented us from directly examining the relationship between stress and cortisol levels on non-stress days. As an alternative, we used hand grip strength measurements as an indirect indicator of stress and potential fluctuations in cortisol levels.\u003c/p\u003e\u003cp\u003eAlthough none of the analyses of maximal or averaged hand grip tension revealed statistically significant differences (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-D), the scatter plots from the GraphPad Prism analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-D) showed some interesting trends for discussion or interpretation:\u003c/p\u003e\u003cp\u003e\u003cb\u003e1)\u003c/b\u003e The mean-averaged trial records from the 21 morning exam sessions were higher (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA) than those from the non-exam morning sessions. However, this difference did not reach statistical significance due to variability and the small sample size. However, the slopes representing muscle tension decline (fatigue) were statistically significantly different between the two conditions. These results suggest that naturally elevated cortisol levels in the morning, when combined with additional cortisol released in response to exam-related stress, may enhance initial force production by motor units in the forearm. However, this heightened activation appears to be less sustainable over time, leading to accelerated fatigue.\u003c/p\u003e\u003cp\u003e\u003cb\u003e2)\u003c/b\u003e A somewhat similar trend is observed in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD, where we compared the same 21 participants\u0026rsquo; mean-averaged trial records from the morning exam session with those from the afternoon exam session. The predicted naturally elevated cortisol levels in the morning likely contributed to increased initial grip tension, although this difference did not reach statistical significance. The average tension was higher in the morning, and the rate of fatigue was similar between sessions, as indicated by non-significant differences in slope (p\u0026thinsp;=\u0026thinsp;0.4263).\u003c/p\u003e\u003cp\u003eOf our three initial hypotheses: \u003cb\u003e(1)\u003c/b\u003e that students\u0026rsquo; maximal grip strength and sustained force would decrease on stressful examination days compared to non-examination days was not supported by our findings; \u003cb\u003e(2)\u003c/b\u003e that students\u0026rsquo; maximal grip strength and sustained force would decrease on stressful morning examination days compared to stressful afternoon examination days was partially supported (specifically, we observed a significantly steeper fatigue slope in the morning sessions, indicating accelerated fatigue); and \u003cb\u003e(3)\u003c/b\u003e the observed differences in our second hypothesis provide preliminary support for the idea that shifting examinations to the afternoon may reduce student stress and potentially improve exam performance. However, our confidence in this recommendation is limited due to some constraints and limitations encountered during the study\u0026rsquo;s design and data collection processes (see Limitations section).\u003c/p\u003e\u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, cortisol levels present in the morning appear to enhance the muscle\u0026rsquo;s ability to generate tension. However, this trend did not reach statistical significance, likely due to the small sample size. When examining the slope of the morning data, we observed that exam conditions significantly increased the rate of fatigue, a finding that was statistically significant only during the morning sessions. In contrast, no such effect was observed in the afternoon sessions. This suggests that administering exams in the afternoon may be less physiologically stressful, as muscle fatigue (calculated using our slope-based method in GraphPad Prism) was not significantly impacted by exam-related stress during that time of day.\u003c/p\u003e\n\u003ch3\u003eLimitations\u003c/h3\u003e\n\u003cp\u003eThis study was designed to investigate whether acute academic stress affects physical performance, using hand-grip strength as a proxy measure. While we were unable to directly assess academic performance due to limitations in accessing student grades, grip strength served as a physiological indicator of how students' bodies responded to stress on exam versus non-exam days. By comparing morning and afternoon sessions, we aimed to determine whether exam timing influences fatigue and, by extension, academic readiness. Our findings suggest that morning exams may be associated with greater fatigue, possibly due to elevated stress levels. This raises the possibility that scheduling exams in the afternoon could reduce stress and support better overall performance, although further research is needed to confirm this hypothesis.\u003c/p\u003e\u003cp\u003eSeveral limitations affected the outcomes of this study. An initial power analysis showed that at least 68 participants completing all four sessions were needed to achieve strong statistical significance. However, only 21 students enrolled, and just 12 completed all required data collections to form a complete dataset for each participant. A smaller sample size than what the power analysis projected reduces statistical power, making it harder to detect real effects and increasing the chance of Type II errors (false negatives). As a result, the study might not identify genuine differences or relationships, even if they exist in the population. This limited sample size likely decreased both the statistical power and the generalizability of the results. Additionally, budget restrictions prevented offering financial incentives, which could have negatively impacted participant recruitment.\u003c/p\u003e\u003cp\u003ePotential sources of bias should also be acknowledged as limitations of this study. The Hawthorne effect may have led participants to exert greater effort during hand grip assessments due to the awareness of being observed. Additionally, stress levels could have been influenced by external academic pressures, such as simultaneous exams or assignments in other courses, as well as by personal life stressors. Measurement timing varied within a two-hour testing window, meaning some students were assessed closer to the exam start time than others, potentially affecting both stress levels and grip performance. Other uncontrolled variables, such as caffeine intake, sleep duration, and overall health status, may also have influenced the grip strength outcomes.\u003c/p\u003e\u003cp\u003eDue to study constraints, our investigation focused solely on hand grip strength measurements using a dynamometer, without collecting data on academic performance or exam outcomes. Although this represents a limitation, previous research has identified correlations between muscle fatigue and cognitive decline, particularly in individuals with pre-existing cognitive impairments [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. These findings support the rationale for investigating grip strength as a potential indirect indicator of academic readiness under stress. However, our study was limited by the lack of access to students' academic grades and physiological markers such as cortisol levels. Incorporating these measures prior to each grip strength assessment could have offered deeper insight into the interplay between stress, physical performance, and academic outcomes.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eFollowing data collection at the Idaho College of Osteopathic Medicine and statistical analysis in collaboration with Boise State University and Sam Houston State University College of Osteopathic Medicine, we found no statistically significant differences in maximum or average hand grip strength between exam and non-exam days. However, a significant difference was observed in the slope of muscle fatigue, specifically in morning exam vs non-exam days, where the rate of decline in grip strength was increased when students had an exam as a stress factor. Additionally, these statistically significant changes become apparent when the regression analysis commences at the 2-second mark. This was not observed when using a 5-second starting point, emphasizing the impact of the analytical approach on observed outcomes.\u003c/p\u003e\u003cp\u003eWhile most of our initial hypotheses were not supported, the finding of accelerated fatigue during morning exam sessions suggests that exam timing may influence physical manifestations of stress. As a student-led study conducted with limited resources, we acknowledge the constraints but also believe that this project makes a meaningful contribution to the field of medical education research. It provides a foundation for future work involving larger, more representative samples and more robust physiological and academic data. Given the global and ongoing challenge of academic stress in medical training, continued research is essential to better understand and mitigate its effects on student well-being and performance.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e: This project was supported by the Idaho College of Osteopathic Medicine (ICOM) Research Department through the Mentored Research Grant (MRG) Program, which provided up to $3,000 for data acquisition and materials and up to $3,000 for student wages ($15/hour for up to 200 hours). Grant funds were used to purchase three Go Direct® Hand Dynamometers, three USB-C to USB Apple Adapters, and one departmental license of Vernier Graphical Analysis Pro. We thank Boise State University’s IRB for reviewing and approving the human subject protocol. We are also grateful to Dr. Jacob Kammer (ICOM) and Dr. Megan Null (Boise State University) for their support with R software analysis and statistical interpretation. The authors further acknowledge funding support from Sam Houston State University College of Osteopathic Medicine (SHSU-COM) for covering the Open Access publication fee, enabling this manuscript to be made freely available upon publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests:\u003c/strong\u003eThere were no competing or conflicts of interest for any of the authors of this study during or after the completion of this research\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCRediT author statement:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDominic Giandonato (DG):\u003c/strong\u003e Conceptualization, Methodology, Investigation, Formal Analysis, Writing – Original Draft, Funding Acquisition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNicholas Rincon (NR):\u003c/strong\u003e Investigation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNathan Adamietz (NA):\u003c/strong\u003e Investigation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMihail Mitov (MM):\u003c/strong\u003e Conceptualization, Methodology, Formal Analysis, Writing – Original Draft, Visualization, Funding Acquisition, Supervision.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eK\u0026ouml;tter, T., et al., \u003cem\u003ePerceived Medical School stress of undergraduate medical students predicts academic performance: an observational study.\u003c/em\u003e BMC Medical Education, 2017. \u003cstrong\u003e17\u003c/strong\u003e(1): p. 256.\u003c/li\u003e\n\u003cli\u003eKhan, M., \u003cem\u003eEffect of Perceived Academic Stress on Students\u0026rsquo; Performance.\u003c/em\u003e 2018.\u003c/li\u003e\n\u003cli\u003eLopes Dos Santos, M., et al., \u003cem\u003eStress in Academic and Athletic Performance in Collegiate Athletes: A Narrative Review of Sources and Monitoring Strategies.\u003c/em\u003e Frontiers in Sports and Active Living, 2020. \u003cstrong\u003e2\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eAssmann, M., et al. \u003cem\u003eComparison of Grip Strength in Recreational Climbers and Non-Climbing Athletes\u0026mdash;A Cross-Sectional Study\u003c/em\u003e. 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Abdelazeim, and S. Elshennawy, \u003cem\u003ePhysical and cognitive consequences of fatigue: A review.\u003c/em\u003e Journal of Advanced Research, 2015. \u003cstrong\u003e6\u003c/strong\u003e(3): p. 351-358.\u003c/li\u003e\n\u003cli\u003eKobilo, T. and H. Van Praag, \u003cem\u003eMuscle Fatigue and Cognition: What is the Link?\u003c/em\u003e Frontiers in Physiology, 2012. \u003cstrong\u003e3\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003e\u0026Scaron;piljak, B., et al. \u003cem\u003eA Review of Psychological Stress among Students and Its Assessment Using Salivary Biomarkers\u003c/em\u003e. Behavioral Sciences, 2022. \u003cstrong\u003e12\u003c/strong\u003e, DOI: 10.3390/bs12100400.\u003c/li\u003e\n\u003cli\u003eLaw, R. and A. Clow, \u003cem\u003eChapter Eight - Stress, the cortisol awakening response and cognitive function\u003c/em\u003e, in \u003cem\u003eInternational Review of Neurobiology\u003c/em\u003e, A. Clow and N. Smyth, Editors. 2020, Academic Press. p. 187-217.\u003c/li\u003e\n\u003cli\u003eChauhan, S., et al., \u003cem\u003eBeyond sleep: A multidimensional model of chronotype.\u003c/em\u003e Neuroscience \u0026amp; Biobehavioral Reviews, 2023. \u003cstrong\u003e148\u003c/strong\u003e: p. 105114.\u003c/li\u003e\n\u003cli\u003eBini, J., et al., \u003cem\u003eStress-level glucocorticoids increase fasting hunger and decrease cerebral blood flow in regions regulating eating.\u003c/em\u003e NeuroImage: Clinical, 2022. \u003cstrong\u003e36\u003c/strong\u003e: p. 103202.\u003c/li\u003e\n\u003cli\u003eSharma, A., et al., \u003cem\u003eCortisol affects macrophage polarization by inducing miR-143/145 cluster to reprogram glucose metabolism and by promoting TCA cycle anaplerosis.\u003c/em\u003e Journal of Biological Chemistry, 2024. \u003cstrong\u003e300\u003c/strong\u003e(10): p. 107753.\u003c/li\u003e\n\u003cli\u003eRodi\u0026ntilde;o-Janeiro, B.K., et al., \u003cem\u003eAcute stress triggers sex-dependent rapid alterations in the human small intestine microbiota composition.\u003c/em\u003e Frontiers in Microbiology, 2025. \u003cstrong\u003e15\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eRoca Rubio, M.F., et al., \u003cem\u003eAssociations between various markers of intestinal barrier and immune function after a high-intensity exercise challenge.\u003c/em\u003e Physiological Reports, 2024. \u003cstrong\u003e12\u003c/strong\u003e(10): p. e16087.\u003c/li\u003e\n\u003cli\u003eAnderson, G.S., et al., \u003cem\u003eThe Impact of Acute Stress Physiology on Skilled Motor Performance: Implications for Policing.\u003c/em\u003e Frontiers in Psychology, 2019. \u003cstrong\u003e10\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eGallistl, M., et al., \u003cem\u003eEvidence for differential associations of distinct trait mindfulness facets with acute and chronic stress.\u003c/em\u003e Psychoneuroendocrinology, 2024. \u003cstrong\u003e166\u003c/strong\u003e: p. 107051.\u003c/li\u003e\n\u003cli\u003eFaresj\u0026ouml;, \u0026Aring;., et al., \u003cem\u003eHigher hair cortisol levels associated with previous cardiovascular events and cardiovascular risks in a large cross-sectional population study.\u003c/em\u003e BMC Cardiovascular Disorders, 2024. \u003cstrong\u003e24\u003c/strong\u003e(1): p. 536.\u003c/li\u003e\n\u003cli\u003eCvijetic, S., et al., \u003cem\u003eDiurnal Salivary Cortisol in Relation to Body Composition and Heart Rate Variability in Young Adults.\u003c/em\u003e Frontiers in Endocrinology, 2022. \u003cstrong\u003e13\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eZhu, K., et al., \u003cem\u003eAssociations between hypothalamic\u0026ndash;pituitary\u0026ndash;adrenal axis function and peak bone mass at 20years of age in a birth cohort.\u003c/em\u003e Bone, 2016. \u003cstrong\u003e85\u003c/strong\u003e: p. 37-44.\u003c/li\u003e\n\u003cli\u003eDovom, M.M., et al., \u003cem\u003eEffects of Official Chess Competition on Salivary Cortisol and Mood Swings in Adolescent Girls: A Win\u0026ndash;Loss Approach.\u003c/em\u003e Applied Psychophysiology and Biofeedback, 2024. \u003cstrong\u003e49\u003c/strong\u003e(2): p. 301-311.\u003c/li\u003e\n\u003cli\u003eHannibal, K.E. and M.D. Bishop, \u003cem\u003eChronic Stress, Cortisol Dysfunction, and Pain: A Psychoneuroendocrine Rationale for Stress Management in Pain Rehabilitation.\u003c/em\u003e Physical Therapy, 2014. \u003cstrong\u003e94\u003c/strong\u003e(12): p. 1816-1825.\u003c/li\u003e\n\u003cli\u003eCastillo-Navarrete, J.L., A. Guzm\u0026aacute;n-Castillo, and C. Bustos, \u003cem\u003eLongitudinal analysis of academic stress and its effects on salivary cortisol, alpha-amylase, and academic outcomes: Study protocol.\u003c/em\u003e PLOS ONE, 2024. \u003cstrong\u003e19\u003c/strong\u003e(12): p. e0315650.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Medical School, Summative Assessment, Academic Stress, Hand Grip Measurements, Force Output, Muscle Fatigue","lastPublishedDoi":"10.21203/rs.3.rs-6985107/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6985107/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMedical schools are using summative examinations to evaluate the students\u0026rsquo; knowledge and competencies acquired in various biomedical and clinical domains throughout medical education. These examinations are necessary, but they are known to evoke high levels of stress in the students involved. Previous studies have shown that increased stress levels can negatively impact participants' cognitive and physical abilities, resulting in a decrease in overall performance. Our study was designed to test this relationship by comparing student hand grip strength output and fatigue rates using grip strength on stressful (exam) and non-stressful (non-exam) days.\u003c/p\u003e\u003cp\u003eFirst-year medical students were recruited to squeeze a hand dynamometer at maximum muscle tension for 30 seconds three separate times. Students gave these readings on four separate days, including: morning examination/non-examination and afternoon examination/non-examination. Data sets were completed using a Vernier Go Direct Hand Dynamometer and Vernier Graphical Analysis Pro program. Statistical analysis was initially completed using GraphPad Prism, where the slope, the area under the curve (AUC), maximum, and average force were initially analyzed. The experimental data were further analyzed using analysis of variance (ANOVA), fitted with simple regression lines for the slope of fatigue for each pair of conditions, and a mixed linear model was created and tested using R statistical software.\u003c/p\u003e\u003cp\u003eA total of 207 recorded hand grip profiles from 21 students were collected; 12 students completed the entire study (provided all measurements consisting of four days of 3 data sets each). These results showed no statistical significance when examining the maximum or average peak force and the area under the curve of the grip profiles. The analysis using simple regression in GraphPad Prism for fatigue (or force rundown) revealed a statistically significant effect in the morning data sets only, where, on exam days, fatigue was accelerated (slopes differed, p\u0026thinsp;=\u0026thinsp;0.0013).\u003c/p\u003e\u003cp\u003eThe main statistically significant finding was an accelerated rate of hand grip fatigue in the morning but not on the afternoon exam days, suggesting that exams might be less stressful in the afternoon. Scheduling stressful exams in the afternoons might not affect muscle hand grip as much as exams in the morning and might correlate with better student academic outcomes.\u003c/p\u003e","manuscriptTitle":"Impact of Exam Stress and Time of Day on Hand Grip Fatigue in Medical Students","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-01 08:49:50","doi":"10.21203/rs.3.rs-6985107/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-08T06:06:03+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-01T15:51:21+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-28T14:47:41+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-24T08:15:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"192088958522659392171225971414511132904","date":"2025-08-24T08:01:36+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-23T08:30:25+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-21T10:44:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"61917437573486607617485074780367011814","date":"2025-08-21T10:27:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"93332494014822467744675556442508439991","date":"2025-08-21T10:10:45+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-20T20:30:55+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-20T14:39:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"6106316656810429807521630588490392723","date":"2025-08-20T13:19:31+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-20T06:29:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"76553298695156443998543223119906664065","date":"2025-08-20T05:56:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"280971879311921583434346086717973602122","date":"2025-08-19T14:38:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"163401072222661834506781748144616249071","date":"2025-08-19T11:10:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"200562202442086952148200143158874258537","date":"2025-08-19T10:21:45+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-19T09:01:38+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-19T08:14:09+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-22T16:29:26+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-16T17:11:51+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-07-16T16:11:35+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e4762566-6491-4254-bbd2-0ed4bb523b08","owner":[],"postedDate":"September 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":53621104,"name":"Health sciences/Health care"},{"id":53621105,"name":"Health sciences/Medical research"},{"id":53621106,"name":"Biological sciences/Psychology"},{"id":53621107,"name":"Social science/Psychology"}],"tags":[],"updatedAt":"2026-01-12T16:07:44+00:00","versionOfRecord":{"articleIdentity":"rs-6985107","link":"https://doi.org/10.1038/s41598-025-33886-8","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-01-07 15:57:11","publishedOnDateReadable":"January 7th, 2026"},"versionCreatedAt":"2025-09-01 08:49:50","video":"","vorDoi":"10.1038/s41598-025-33886-8","vorDoiUrl":"https://doi.org/10.1038/s41598-025-33886-8","workflowStages":[]},"version":"v1","identity":"rs-6985107","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6985107","identity":"rs-6985107","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}