Friday, July 12, 2024

    What is the Mental Health Benefits of Voluntary Physical Activity on University Students? An Examination of State anxiety and Positive Affect.

    DISCLAIMER: The following results of this independent pilot study are unpublished and were conducted under the supervision at the University of Western, School of Kinesiology. This study was self funded by the author and hence presents an inherent conflict of interest.


    Previous research suggests that physical activity may influence the management of individual mental health status, especially those suffering from states of depression and anxiety (Paluska, 2000, p. 167). Physical inactivity has increased susceptibility to chronic illnesses, pre-mature death, and diminished mental health status. These implications may support the importance of establishing active lifestyles during childhood and early adulthood (Irwin, 2004, p.1). Irwin et al. (2004) performed a systematic review of university students’ participation in physical activity to determine the level needed to acquire health benefits. The results in reference to the American College of Sports Medicine (ACSM) guidelines inferred that more than one-half of university students in Canada, United States and internationally are not active enough to gain health benefits, with female students being less active than male students (2004, p.939). Few studies have explored the outcomes of physical activity in the context of improving mental health status (Trost, 2002, p.5). 

    There is evidently a need for the construction and implementation of a standardized tool using the ACSM prescription to measure, track and compare student physical activity patterns (Irwin, 2004, p.940). A more recent systematic review suggested that the nature of the physical activity should be from moderate to vigorous intensity to provide sufficient mental health benefit. Aerobic activities stressing the cardiovascular and respiratory systems were shown to have the greatest benefit (Janssen, 2010, p. 13). Paluska et al. (2000) reported aerobic training and strength/flexibility training to have equal efficacy for treating symptoms of depression and anxiety. Studies generally support that acute anxiety responds better to exercise than chronic anxiety. The patterns of training involved with physical activity and exercise may recruit a process which supports enduring resilience to stress (Salmon, 2001). More studies are needed to clarify the mental health benefits of exercise among populations and processes underlying those benefits of physical activity on mental health. 

    The aim of this study was to examine how voluntary physical activity affects state anxiety and positive/negative affect of university students. State anxiety is the unpleasant emotional arousal one faces when confronted with a demanding or threatening situation. The prerequisite for the experience of this emotion is one’s cognitive appraisal of threat (Lazarus, 1991). Positive affect is the extent one subjectively experiences positive moods (Ex. Joy, interest, and alertness) (Goldstein, 2011). It can be measured using an affect balance scale (i.e. ranging from positive to negative affect) that indicates ones feeling or subjective well-being (SWB). SWB refers to how people experience the quality of their lives including both emotional reactions and cognitive judgments (Diener, 2003). It was hypothesized that physically active students will experience lower state anxiety and higher positive affect scores relative to when they are physically inactive. Further, the 12 variables taken from the Diagnostic and Statistical Manual of Mental Disorders, 4th edition(DSM-IV) defining a complete state model of mental health (Appendix B) would positively correlate with the degree of physical activity. Given the breadth of literature supporting the acute positive outcomes of physical activity, it is predicted that physical activity will improve student mental status. 


    Participants and Design 

                This study was conducted to evaluate evidence regarding the influence that physical activity has on student mental health status. The study consisted of a repeated measures design conducted using the Experience Sampling Method (ESM) (Hektner, 2007). Data were collected from a convenience sample of 19 fourth year undergraduate students (9 male, 10 female) at The University of Western Ontario.  ESM is a research procedure for studying what participants do, feel and think during their daily lives. It consists of asking participants to stop at certain times and self report their experience in real time. Sets of these self-reports from participants form an aggregate dataset that defines daily experiences (Hektner, 2007).  The inclusion criterion involved students in fourth year undergraduate studies. After participants were briefed on the study procedures, consent regarding subject participation was obtained.  Each participant was permitted to withdraw at any time. 


    The measures used to evaluate state mental health status for participants were the 100mm Visual Analog Scale based on Kindler et al. (2000) for anxiety, the Affect Balance Feeling Scale based on Hardy et al. (1989), and DSM-IV variables evaluated by Keyes et al. (2005), defining the complete state model of mental health (Appendix B). For the Visual Analog Scale measuring anxiety, each participant marked their level of anxiety ranging from 0 (Not anxious at all) to 100 (Most anxious I can imagine) millimeters. The affect balance scale measured feeling by having participants self-report a score ranging from -5 (Very Bad) to +5 (Very Good). The 12 variables defining the state model of mental health from the DSM-IV were examined using a  Likert scale, which ranged from 1 meaning “strongly disagree” to 5 meaning “strongly agree”, with 3 being neutral (Blake, 1995). The scoring sheet and explanations for the DSM-IV variables can be seen in Appendix A and B. The measures used to evaluate physical activity were based on self-report measures on domains including: physical activity engagement status (Active or Inactive), relative timing  of physical activity (Range of ≤0.5 – 6 hours ago), duration (Range of ≤15 – >60 minutes), type (Cardio, Strength Flexibility or Other), intensity (Low, Moderate or High), and group vs. independent orientation. 


    Each participant was given a  self-report booklet (Appendix A) which they were instructed to carry around and record scores three times daily for two weeks. Each booklet was marked with a sex identification label of 1 (male) or 2 (female). A method of contact was established for each participant (e.g. Cell phone or email) and electronic reminders to record self-rated measures in booklets were automatically sent in the morning, afternoon and evening at 9:00am, 3:00pm and 9:00pm respectively. These prompts were sent out the entire duration of the study (two weeks). Prompts were sent through email and by text message using a cellular phone by the researcher using a group-messaging technique. When participants received these prompts they were instructed to complete their self-rated measures (Appendix A).  Each scoring sheet was set to be used six hours apart. Separating times six hours apart ensured that tracking of individual state domains of mental health could be achieved in real time. When prompted, each participant completed their scoring sheet for all self-rated measures. Participants were instructed to carry on with their daily routine, including physical activity as was typical for them. Booklets were collected the day after the study for database input and statistical analysis. A gift card incentive program was used to sustain and promote participant adherence. Participants were allowed to participate in activities of their choice during the study.

    Statistical Analyses 

    The effect of physical activity on each domain of state mental status, anxiety and affect balance was analyzed using Microsoft Excel (2016) software. All data logged for the 19 participants’ booklets were collected and manually entered into a custom built excel database. A correlation matrix was first created for all variables to observe evidence pertaining to the influence of participant active states on mental health and behaviour outcome measures including anxiety, affect balance, and the 12 DSM-IV variables. Means were reported for the 12 DSM-IV variables across all participants for total (mean) reported active days. These variables were further coded into datasets and individually extracted from the excel database using Visual Basic for Applications (VBA programming language) for further analytics. Means were reported for Anxiety Scores and  Affect Scores plus or minus one standard deviation of the mean. Total days active and total days inactive were calculated for each participant as indicated by their self-report. Alpha value for significance was selected at P < 0.05. Incomplete data coded as “NA” was removed from the dataset using VBA programming language. Mean anxiety and positive affect feeling scores were evaluated using a two-tailed paired t-test to indicate statistical significance.


    Average Physical Activity of all participants 

    Study participants recorded the duration of physical activity for sessions reported as active. The mean duration of physical activity for all participants during active days in this study was calculated to be ~ 28 minutes per day. All participants on average spent approximately 3 days active over the duration of the study suggesting most participants were minimally to never active. 

    Examining Mean Domains of Mental Health on Days Active 

    During participant self-reported active days, positive affect (variable one)measured by the affect balance scale had the strongest positive correlation (0.238) while anxiety (variable two) measured by the visual analog scale had the strongest negative correlation  (-0.222) on active days. Among the DSM-IV variables, social contribution (0.176) and happiness/satisfaction (0.156) mean scores showed the most prominent correlations on days active across participants. Overall, weak positive correlations were observed across all means of the 12 domains of mental health described in Figure 1 (variables 3-14). 

    Figure. 1. Correlation of mental health domains for days active across participants.  Scores were calculated using the visual analog scale for variable 1, feeling scale for variables 2, and Likert scale for domains 3-14. Each participant manually recorded their score three times and whether they were active. Scores for, feeling, anxiety and the domains of mental health are plotted according to days active across participants. Data are reported as correlation coefficients. 

    Participant Mean Anxiety and Feeling Scores

    All means, standard deviations, and minimum and maximu scores for each participant’s anxiety and feeling scores were calculated (see Table 1). The mean anxiety score and standard deviation of each participant was calculated along with the highest and lowest  recorded anxiety score in each individual series of data (see Figure 2). Participants 1-9 were male and 10-19 were female. In Figure 2, participant 6 experienced the highest mean anxiety score of 63 ±12recording a median score of 61. His highest  anxiety score was 89 and lowest anxiety score was 40. Conversely, participant 18 exhibited the lowest mean anxiety score of 5.4 ±4 recording a median score of 36. Her highest anxiety score was 21 and lowest anxiety score was 2. Participant 9 had the highest standard deviation of  ±30 from their mean anxiety score of 51.Through averaging the means of anxiety for male and female, it was observed that females had a lower average of 18  ±15 compared with males at 34 ±15. However, female mean anxiety scores were not found to be statistically significant (t=1.64, p=0.118) in this study compared to males. The mean anxiety scores differed among participants, and individuals with higher levels of anxiety showing more variability compared to those with lower levels. The overall range for mean anxiety scores among participants was 56. Individually, participant 9 exhibited the highest range (92) and participant 18 displayed the lowest range (20). The overall mean anxiety score for all participants was 28 ±15. 

    The mean feeling score and standard deviation of each participant was also calculated for each participant (see Table 1). In Figure 3, participant 2 experienced the lowest mean feeling score of -1.14.  His highest feeling score was +3 and lowest score was -5.  Participant 18 exhibited the highest mean feeling score of +3.9 (as well as the lowest mean anxiety score). Her highest feeling score was +4 and her lowest score was +1. Participant 9 had the highest standard deviation of ±2.25 from their mean feeling score of +0.62. Overall, participants with higher feeling scores seemingly showed slightly less variability compared to those with lower feeling scores. The overall range for feeling score was 4.47, with participant 9 exhibiting the highest range and participant 18 exhibiting the lowest range. The overall mean feeling score for all participants was 1.81 ±1.21. However, female mean feeling scores were found to be  significantly different (t=2.46, p=0.024) compared to males. Female participants reported a mean score of +3.86 relative to males who presented a lower mean score of +1.69.  

    ParticipantMean Anxiety ScoreSD (Anxiety)Mean Feeling ScoreSD (Feeling Score)
    Table 1. Mean and Standard Deviation of Feeling and Anxiety scores for participants. Scores were calculated using Feeling Scale and Visual Analog Scale. The feeling and anxiety scores for each participant were averaged.Participants 1-9: Male; Participants 10-19: Female. 
    Figure 2. Mean anxiety scores for participants over two-week trial. Scores were calculated using Visual Analog Scale. Each participant manually recorded their anxiety levels three times (9:00am, 3:00pm and 9:00pm) daily for two weeks. The anxiety scores for each participant were averaged. Data is reported as mean anxiety levels ± SD. Error bars represent +1 standard deviation. Participants 1-9: Male; Participants 10-19: Female. 
    Figure 3. Mean Feeling scores for participants over two-week trial. Scores were calculated using the Affect Balance Feeling Scale. Each participant manually recorded their feeling (positive or negative) three times (9:00am, 3:00pm and 9:00pm) daily for two weeks. The feeling scores for each participant were averaged. Data is reported as mean anxiety levels ± SD. Error bars represent +1 standard deviation. Participants 1-9: Male; Participants 10-19: Female . 

    Variability of Participant Anxiety and Feeling Scores in Real Time

    Participant anxiety scores were self-reported in the morning (9:00am), afternoon (3:00pm) and evening (9:00pm). ~42 real time anxiety scores were recorded over the two weeks following the commencement of the study. In Figure 4, participants 1 and 2 are male and participants 9 and 18 are female. Participant 1 exhibited a low stable anxiety level with increased fluctuations followed by a re-stabilization of anxiety level. This participants’ anxiety score reaches as high as 69 and as low as 36 with a standard deviation of 12. The same was observed with participant 18 whose anxiety score reached as high as 22 and as low as 2 with a standard deviation of 4. The range for participant 1 and 18 was 65 and 41 respectively.  Participant 2 seems to exhibit high unstable anxiety levels with a standard deviation of 21 with the highest score being 92 and the lowest score being 16. These levels seem to be highly variable over the span of two weeks relative to Participant 1. Hence, base anxiety levels, along with fluctuation in anxiety levels, varied highly among individual subjects. The same patterns were observed with participant 9 who exhibited unstable anxiety levels with a standard deviation of 30 with the highest score reported as 98 and the lowest score as 54. The range of participant 2 and 9 was 22 and 41 respectively. In Figure 5, participant 1 exhibited what seems to be a stable positive feeling score with an average positive affect score of 2.60 ±1.12 over the two-week period. The same findings were observed for participant 18 who reported an average positive affect score of 3.90 ± 0.67. In contrast, participants 2 and 9 reported feeling scores that were highly variable. Participant 2 reported an average negative affect score of -1.14 ±1.44 while participant 9 reported a low positive affect score of 0.62 ±2.26 over the two-week period.

    Figure 4. Anxiety scores for participants during daily living.  Participants 1 and 2 are male and participants 9 and 18 are female. Scores were calculated using Visual Analog Scale. Each participant manually recorded their anxiety levels three times (9:00am, 3:00pm and 9:00pm) daily for two weeks. Anxiety scores are plotted in real time over ~42 data points. Data are reported as mean anxiety levels.
    Figure 5. Feeling scores for participants during daily living.  Participants 1 and 2 are male and participants 9 and 18 are female. Scores were calculated using Affect Balance Scale. Each participant manually recorded their state of feeling three times (9:00am, 3:00pm and 9:00pm) daily for two weeks. Feeling scores are plotted in real time over ~42 data points. Data are reported as mean Feeling scores.
    Physical Activity and Mea

    Physical Activity and Mean Anxiety

                The mean anxiety scores for active and inactive days were calculated for each participant. In Figure 3, participant 2 reported 4 days of being active with a mean anxiety of 45 ±19 and 9 days of inactivity with a mean anxiety score of 70 ± 18. This represents that participant 2 had a lower mean anxiety on the days they were active compared to those days they were inactive. The results can be seen with participant 12 who exhibits a decrease in anxiety of approximately 32 on the days they were active (~3.6 total) relative to inactive (~10.4 total). The mean anxiety score for all participant active days was reported as 23 compared to 30 for inactive days. A t-test was performed and found anxiety scores between active and inactive conditions to be statistically significant different (t = 6.172, p=0.014).

    Figure 5. Comparison of anxiety score between active and inactive days.  Scores were calculated using Visual Analog Scale. Each participant manually recorded their anxiety levels three times and whether they were active or inactive respectively at those times. Anxiety scores for mean active days are plotted with anxiety scores on mean inactive days. Data are reported as mean anxiety levels.

    Physical Activity and the Affect Balance Scale 

    Participants self-reported whether they were active or inactive three times daily in conjunction with the feeling score measure examining positive and negative affect. Mean feeling scores for active days were plotted along with mean feeling scores for inactive days for each participant for the two-week study. In Figure 6, it is observed that participant 12 reported a mean active feeling of 4.33 ±0.58 (Very Good) and a mean inactive feeling of 0.30 ±1.80 (Neutral). Moreover, participant 2 exhibited a mean active feeling score of 0.857 ±1.29 (Fairly Good) and an inactive feeling score of -2 ±1.6 (Bad). The mean score for all participant active days was reported as 2.2 compared to 1.2 for inactive days. A t-test showed that mean feeling scores were significantly more positive for active days than for inactive days (t= 6.164, p=0.011).

    Figure 6. Comparison of feeling score between active and inactive days.  Scores were calculated using the affect balance scale. Each participant manually recorded their feeling score three times and whether they were active or inactive respectively at those times. Feeling scores for mean active days are plotted with feeling scores on mean inactive days. Data are reported as mean feeling scores ranging from -5 (Very Bad) to +5 (Very Good)


    This research explored the positive benefits of physical activity on student mental health and wellness by examining anxiety variability of anxiety and feeling data in real time and analyzing the relationship of physical activity data on mean anxiety and positive affect data for active vs. inactive days. First, it was examined that participants with lower mean anxiety scores had less variable scores compared to those with higher mean anxiety scores who expressed increased variability. It was also found that participants with high mean feeling scores had less variability compared to those with lower mean feeling scores. The stability of low state anxiety scores and high feeling scores may imply an individuals’ resilience to stress. This may suggest that participants’ differences in responding to threats could potentially predict anxiety in addition to other demographic factors (Min, 2012). The data showed a wide range of participants exhibiting low to high anxiety levels and feeling scores, which may be reflective of each participant’s unique baseline and tolerance to common environmental stresses. Resilience is the defense mechanism that allows people to overcome adversity (Davydoy, 2010). It was also found in this study that female mean anxiety scores, reported as lower than males, were not statistically significant (p<0.05). Very few studies have examined sex differences and how they may contribute to different state anxiety scores. Results from a study by Bresiau et al. (1995) suggested that the occurrence of anxiety in females was greater than males beginning in early life implying that females are at higher risk for major depression. Further research should examine the impact of sex differences in anxiety disorders. 

    Secondly, when examining anxiety and feeling scores in real-time over ~42 average data points, measures can be both stable and variable. For some participants, a consistent mean anxiety level was observed with a low standard deviation. At times, surges in anxiety were recorded followed by a gradual return to the participant’s baseline anxiety level.  For others, anxiety was highly variable and displayed no consistency between data points. Moreover, it was observed that some participants exhibited a sustained positive affect on average while others over the two-week duration exhibited  negative feelings on average. Overall, it was observed that participants with high mean anxiety scores also reported the most variability and those with low scores reported the least variability. No participants reported a high or moderate average for scores of anxiety and feeling with lower variability. This may have been because of the presence of trait specific characteristics found in mental health disorders related to depression and anxiety. For example, diagnosis of an anxiety disorder such as bipolar disorder was found to be related to high anxiety, highly variable feeling and lower goal attainment scores according to Young et al. (1993). Further research is required to support that of Jadoon et al. (2010) who conducted a cross sectional study on 815 medical students to examine the prevalence of anxiety and depression. The study showed that students constitute a vulnerable group that has high prevalence of anxiety and depression (Jadoon, 2010). Further research should investigate the high-variable and low-stable type streams exhibited by participants and examine their specific activity engagement. Also, female mean feeling scores were found to be significantly more positive than males (p<0.05). Women scored higher on measures reflecting emotional intensity and positive affect compared to males supporting similar findings by Diener et al. (1985). Further studies should examine student anxiety and feeling with gender and the respective year of university studies. 

    When examining mean anxiety levels for active days it was noticed that anxiety levels were significantly lower in comparison to mean anxiety scores for inactive days. A similar trend was observed for feeling scores where active days produced an increase in positive affect whereas inactive days demonstrated a significant decrease in positive affect. The evidence to support findings on anxiety and feeling scores was found to be considerable between active and inactive states. When examining the variables from DSM-IV (Appendix B), a weak correlation was seen across all 12 domains (Appendix B). The strongest correlations were associated with physical activity where there was a decrease in anxiety and increase in positive affect. These findings support those of Aşçı et al. (2003) who investigated the effects of participation in physical fitness programs on anxiety. Through conducting a ten-week fitness program it was found that anxiety among participants significantly decreased. Participants also reported to have a strengthened physical self-perception (Aşçı, 2003). Lastly, the mean duration of physical activity[A16]  for all participants was approximately 28 minutes per day, which is lower than that recommended by ACSM. The data further suggests that partic The Centers for Disease Control and Prevention and the ACSM recommend a minimum 30-60 minutes of moderate intensity physical activity five days per week or 20-60 minutes of high-intensity exercise three days per week (Nelson, 2007). 

    The main findings of this study are that anxiety data streams are: 

    • consistent for some participants and inconsistent for others, student variability of anxiety and positive affect data in real time is high;
    • methods for monitoring and tracking should be implemented to reduce these levels;
    • physical activity can significantly reduce state anxiety and improve positive affect in university students;
    • And there is a positive correlation of physically active participant states with the 12 variables that define a complete state of mental health. 

    Limitations of this study include a small sample size restricted to one environment. Further studies should incorporate larger sample sizes of different age groups across multi-educational environments.  In conclusion, the hypothesis was supported by the majority of participants who reported lower mean anxiety scores and increased positive affect on active days in contrast to inactive days. There needs to be a close examination of the impact of physical activity in different life stages such as high school and post-secondary education where behavioral patterns are developed and instilled (Irwin, 2004). Also, promoting and creating access to physical activity programs that target anxiety reduction and time management may also be an effective strategy for reducing academic stress in students (Misra, 2000). Only then can students seek to lower mean anxiety scores and understand the determinants to reduce the variation of these levels in real time and promote resilience. 

    Future Research 

    Future research should focus on specifying which physical activity programs improve and sustain mental health status for students. Studies should examine program effectiveness on individuals with low state anxiety and feeling scores as well as high inactivity. Further conditions should also be evaluated. For example, evidence supports that changes in monoamine metabolism (noradrenaline, serotonin, dopamine) support the pathophysiological model of depression  (Cramer, 2013). Furthermore, a study by Streeter et al. (2012) examined that these chemical imbalances related to decreased parasympathetic nervous system (PNS) activity and increased sympathetic nervous system activity (SNS) in the autonomic nervous system (ANS). The study found that other imbalances associated with depression included low heart rate variability (HRV), increased hypothalmic-pituitary-adrenal (HPA) axis activity, increased cortisol and reduced neurotransmitters including gamma-aminobutyric acid (GABA) (Streeter, 2012). Specific programs such as yoga propose to work through the HPA axis to decrease the stress response through reducing plasma cortisol levels (Cramer, 2013). The HPA axis regulates glucocorticoid levels and pro-inflammatory cytokines using a negative feedback loop. There is a relationship that stress  (Hypercortisolemia) plays with the HPA axis in provoking depression and anxiety in individuals (Thirthalli, 2013). Depression and anxiety reflects may impart an inflammatory state characterized by dysfunctional glucocorticoid feedback inhibition, hyper secretion of corticotropin-releasing hormone (CRH), HPA axis dysfunction, increased circulatory cortisol and production of pro-inflammatory cytokines. Furthermore, sustained anxiety also been shown to deplete neurotransmission in the forebrain to cause the development of depression (Pascoe, 2015). Yoga works through the HPA axis to reduce allosteric load. It stimulates under activity in the PNS, increases inhibitory action of hypoactive GABA in brain pathways and positively impacts structures critical for perception, emotion, and stress regulation (Streeter, 2012). Understanding activities that use the various biological mechanisms that relate to exercise, breathing and meditation should be explored. Aerobic activity for example is known to stimulate the central nervous system by releasing endorphins, monoamines and brain derived neurotrophic factor in the hippocampus which all enhance mental health (Meyer, 2012).  Finding specific practices such as yoga that support anxiety reduction and the stress response through facilitating mechanisms that restore biochemical activity should be further examined. 


    The development of a tool that will allow educational institutions to track mental health and evaluate feeling has long been overdue. The evaluation of one’s wellbeing and personal growth is essential towards long-term sustainability. Services aimed toward helping facilitate a solution to the issue of deteriorating mental health among students are fragmented, lack direction and overall effectiveness. These services are a toolkit for short-term gains on individuals who already suffer from a depressed status of wellbeing. If these strategies do not result in positive outcomes for students, growth will not be sustained. As leaders, the goal should be to make a student’s mental health and well-being a priority; the challenge is how to monitor what students are feeling and how to establish accountability for their experience. Standard surveys alone have not and will not absolve the issue. There are too many questions that inspire analysis rather than plans of action. Mental health evaluations are also impractical in that respect. The conventional surveys do not seem to characterize the underlying reasons for deteriorated mental state – the kind that lead to growth. The question that remains unanswered is why students, who are our future, are drowning in mental health? This question has developed through research from surveys and statistics indicating the rise in rates of anxiety, depression, suicide and suicide attempts. This study has taken a step in the direction of preventing students from deteriorating mental health by encouraging students to incorporate more physical activity in their schedules and have less inactive days. 


    I would like to express my gratitude for the guidance of my supervisor, Dr. Alan Salmoni. Additionally, I would like to extend my thanks to Dr. Kevin Shoemaker for his guidance. Lastly, I would like to thank the University of Western Ontario and the Department of Kinesiology and the Canadian Institutes of Health Research. 

    Appendix A

    A.1 – Scoring Sheet

    Appendix B

                B.1 – 12 Variables Defining a Complete State of Mental Health

    (American Psychiatric Association. (2000). Diagnostic and statistical manual of mental disorders (4th ed., text rev.). Washington, DC)


    Aşçı, F. H. (2003). The effects of physical fitness training on trait anxiety and physical 

    self-concept of female university students. Psychology of sport and 

    exercise4(3), 255-264.

    Blake, D. D., Weathers, F. W., Nagy, L. M., Kaloupek, D. G., Gusman, F. D., Charney, 

    D. S., & Keane, T. M. (1995). The development of a clinician‐administered PTSD scale. Journal of traumatic stress8(1), 75-90.

    Breslau, N., Schultz, L., & Peterson, E. (1995). Sex differences in depression: a role for 

    preexisting anxiety. Psychiatry research58(1), 1-12.

    Cramer, H., Lauche, R., Langhorst, J., & Dobos, G. (2013). Yoga for depression: A 

    systematic review and meta‐analysis. Depression and anxiety30(11), 1068-1083.

    Davydov, D. M., Stewart, R., Ritchie, K., & Chaudieu, I. (2010). Resilience and mental 

    health. Clinical psychology review30(5), 479-495.

    Diener, E., Oishi, S., & Lucas, R. E. (2003). Personality, culture, and subjective well-

    being: Emotional and cognitive evaluations of life. Annual review of 

    psychology54(1), 403-425.

    Diener, E., Sandvik, E., & Larsen, R. J. (1985). Age and sex effects for emotional i

    ntensity. Developmental Psychology21(3), 542.

    Fujita, F., Diener, E., & Sandvik, E. (1991). Gender differences in negative affect and 

    well-being: the case for emotional intensity. Journal of personality and social 

    psychology61(3), 427.

    Goldstein, S., & Naglieri, J. A. (2011). Encyclopedia of child behavior and development

    New York: Springer.

    Irwin, J. D. (2004). Prevalence of university students’ sufficient physical activity: a systematic review. Perceptual and motor skills98(3), 927-943.

    Janssen, I., & LeBlanc, A. G. (2010). Systematic review of the health benefits of physical activity and fitness in school-aged children and youth. International Journal of Behavioral nutrition and physical activity7(1), 40.

    Keyes, C. L. (2005). Mental illness and/or mental health? Investigating axioms of the complete state model of health. Journal of consulting and clinical psychology73(3), 539.

    Kindler, C. H., Harms, C., Amsler, F., Ihde-Scholl, T., & Scheidegger, D. (2000). The visual analog scale allows effective measurement of preoperative anxiety and detection of patients’ anesthetic concerns. Anesthesia & Analgesia90(3), 706-712.

    Lazarus, R. S. (1991). Emotion and adaptation. London: Oxford University Press.

    Martinsen, E., Hoffart, A., & Solberg, Ø. Y. (1989). Aerobic and non‐aerobic forms of 

    exercise in the treatment of anxiety disorders. Stress and Health5(2), 115-120.

    Nelson, M. E., Rejeski, W. J., Blair, S. N., Duncan, P. W., Judge, J. O., King, A. C., … & 

    Castaneda-Sceppa, C. (2007). Physical activity and public health in older adults. 

    Recommendation from the American College of Sports Medicine and the American Heart Association. Circulation.

    Paluska, S. A., & Schwenk, T. L. (2000). Physical activity and mental health. Sports medicine29(3), 167-180.

    Streeter, C. C., Gerbarg, P. L., Saper, R. B., Ciraulo, D. A., & Brown, R. P. (2012). 

    Effects of yoga on the autonomic nervous system, gamma-aminobutyric-acid, 

    and allostasis in epilepsy, depression, and post-traumatic stress disorder. Medical hypotheses78(5), 571-579.

    Strong, W. B., Malina, R. M., Blimkie, C. J., Daniels, S. R., Dishman, R. K., Gutin, B., … & Rowland, T. (2005). Evidence based physical activity for school-age youth. The Journal of pediatrics146(6), 732-737.

    Trost, S. G., Owen, N., Bauman, A. E., Sallis, J. F., & Brown, W. (2002). Correlates of adults’ participation in physical activity: review and update. Medicine and science in sports and exercise34(12), 1996-2001.

    Hardy, C. J., & Rejeski, W. J. (1989). Not what, but how one feels: The measurement of affect during exercise. Journal of Sport and Exercise Psychology11(3), 304-317.

    Hektner, J. M., Schmidt, J. A., & Csikszentmihalyi, M. (2007). Experience sampling method: Measuring the quality of everyday life. Sage.

    Jadoon, N. A., Yaqoob, R., Raza, A., Shehzad, M. A., & Zeshan, S. C. (2010). Anxiety 

    and depression among medical students: a cross-sectional study. JPMA. The Journal of the Pakistan Medical Association60(8), 699-702.

    Meyer, H. B., Katsman, A., Sones, A. C., Auerbach, D. E., Ames, D., & Rubin, R. T. 

    (2012). Yoga as an ancillary treatment for neurological and psychiatric disorders: 

    a review. The Journal of neuropsychiatry and clinical neurosciences24(2), 152-164.

    Min, J. A., Lee, N. B., Lee, C. U., Lee, C., & Chae, J. H. (2012). Low trait anxiety, high 

    resilience, and their interaction as possible predictors for treatment response in patients with depression. Journal of affective disorders137(1), 61-69.

    McAuley E., Mihalko, S. L., & Bane, S. M. (1996). Acute exercise and anxiety reduction: 

    Does the environment matter?. Journal of Sport and Exercise Psychology18(4), 408-419.

    Misra, R., & McKean, M. (2000). College students’ academic stress and its relation to 

    their anxiety, time management, and leisure satisfaction. American Journal of Health Studies16(1), 41.

    Salmon, P. (2001). Effects of physical exercise on anxiety, depression, and sensitivity to 

    stress: a unifying theory. Clinical psychology review21(1), 33-61.

    Young, L. T., Cooke, R. G., Robb, J. C., Levitt, A. J., & Joffe, R. T. (1993). Anxious and 

    non-anxious bipolar disorder. Journal of affective disorders29(1), 49-52.

    Author: Adriano Brescacin, School of Kinesiology, University of Western

    Data Contact:

    Other Articles