The Importance Of Physical Exercise For Mental Health

The Relationship Between Physical Exercise and Mental Health

The positive impact of physical exercise on physical as well as mental health cannot be denied. Physical exercise helps in the regulation of stress and in addition reduces the risk of certain illness like cancer, obesity, stroke and cardiovascular diseases. Samples collected for 1237194 individuals were analysed for mental health by dividing into two categories – individuals who exercised and individuals who did not exercise. The participants of the study were asked to assess their mental health and score them accordingly. The part of the population that agreed to exercising showed lesser days of poor mental health than people who did not exercise.

 The relationship between mental health and exercise was found to be significant (Chekroud et al, 2018). The psychological, cognitive and physiological ability of an individual can be improved with the help of exercise. Physical exercise can also improve the functioning capability of the brain even in the individuals who show no signs of fatigue. The BDNF of brain derived neurotrophic factor in an individual tends to increase after exercising. The BDNF and changes in the brain induced by exercising are positively correlated. An increase in the hippocampal volume is related to the long term memory of an individual. This increase is caused by aerobic exercises among older adults. Regular exercise is directly relate to the improvement of communication and awareness. Physically fit children are also seen to have an increased hippocampal volumes. The cognitive performance of children are enhanced when hippocampal volume increases.

A UK based study shows that the illness related to workplace is very much common among the people. It has been estimated that over a span of 10 years more than five million working days have been lost in healthcare. This study answers the question – “Could exercise improve mental health and cognitive skills for surgeons and other healthcare professionals?” At any given age occupational stress is a very well-known factor that can affect healthcare professionals or clinicians. Even after this, a lot of clinicians are seen to be working under this occupational stress or other psychological problems that include chronic fatigue, anxiety, burnout etc. These medical professionals tend to do so in the sense that they do not want to let down their colleagues. In the case of surgeons, mental health issues are discarded. This is because of the general idea among people that surgeons have a better capability to cope with stress than the people working in other medical specialities and thus being protected from the psychological burnout caused in the workplace. It is known that there are various benefits of exercise on the physical health of an individual. The importance of physical exercise in maintaining a good mental health and well-being often gets underestimated. Regular exercising has a positive effect on the psychological health of an individual that helps in boosting the self-esteem of person and thus helps in fighting clinical depression (Parry et al, 2018).

According to a study conducted by Pascoe et al, (2020), depression seen to decrease among youths who exercise regularly. In the developed countries it is expected that by the year of 2020, mental health disorders among the youth will be the leading cause of disability. Interventions are necessary because young people have the tendency of denying help for mental health related issues. Therefore, if individuals have access to mental health problem related interventions at an early stage of illness, they find it beneficial as it has the ability of improving the mental health status of the individuals and thus help in the prevention of negative impact of the ongoing mental illness. It was found that the effect of exercise had a beneficial effect on the mental health of young people. It has been stated in this study that any physical activity or exercise can be considered as a non-stigmatism intervention which has very few side effects.  Higher level of depression has also been reported by women who do not exercise regularly.

The Cognitive and Psychological Benefits of Exercise

 Apart from the physical activity itself, the duration of a physical activity also has a significant effect on the mental health of individuals (Grasdalsmoen et al, 2020). The aim of this paper is to check the association between running for 30 minutes and the mood of a person. Correlation analysis has been used as the selected measure of indicator.

The mental health of a person depends hugely on physical exercise. Due to the fact that exercise gives an individual an idea of well-being, some people have the habit of exercising regularly. People tend to feel more energetic, feel relaxed and have a positive attitude towards themselves. Physical exercise also helps in fighting certain mental health problems like ADHD and insomnia. In order to understand the association between physical exercise and mental situation, samples from 60 people were collected using a survey. Out of the 60 participants 38 participants were females and 22 participants were males. Approximately 50 participants were of Australian origin. Out of all the participants only 20% of the participants were seen to live alone. 36 participants agreed to following a religion while 24 participants claimed that they were atheists.

Sampling technique is hugely used in any research. Analysing a very large number of dataset is very time consuming and may cause a lot of errors. Therefore, a small sample is chosen from the large population and the properties of the sample are analysed. The results of this analysis helps in reaching a conclusion about the population. Sampling, therefore, is an important part of any analysis. There are certain advantages and disadvantages of sampling. Since a sample size is smaller, its analysis becomes easier, consumes less time and money and also helps in achieving faster results. The chance of occurrence of a bias is the primary disadvantage of sampling. But owing to the advantage that it consumes less time in analysis, sampling is the best to achieve results (Bhardwaj, 2019). In the current study, the participating samples were obtained from social media services like Twitter and Facebook. A survey questionnaire was posted on Twitter as well as Facebook. The data was selected on the basis that the participants positively run at least once a week and are okay about disclosing their personal information. This personal information involves information regarding exercise and mood of the participants.

In the current study a run of at least 30 minutes is considered as a full run. The participants were asked to track the number of complete runs made by them in a span of a month or 30 days. The positive and negative daily mood of the participants were asked to rate on a scale of 0 to 9.

The Human Research and Ethics Committees or HRECs generally review the research proposals involving human participants. This is done in order to ensure that the ethical guidelines and standards are properly followed. A consent for was signed by the participants before taking part in the study which was approved by the Human Research Ethics Committee (Approval No: MOC007). The individuals who took part in the study were made aware of the fact that only a run of minimum 30 minutes would be considered as a full run. The participants were told that within a time span of April to August, they could choose any 30 day period in order to record their running time and mood.

Exercise and the Mental Health of Healthcare Professionals

The aim of this study is to check the association between physical exercise and mood. Pearson’s bivariate correlation is an excellent measure that helps in understanding the association between two variables, which is also used in this study. The variables involved in this study are:

1. Runs: number of completed runs in a month
2. Positive Mood: mean positive mood score in a month
3. Negative Mood: mean negative mood score in a month

A basic idea about the data under study can be obtained with the help of descriptive statistics. The very first step of any statistical research is to calculate the descriptive summary of the considered data, even when the aim of the study is to perform inferential analysis. Frequency distribution tables, frequency tables, measures of central tendency like mean, median and mode and measures of dispersion like standard deviation, variance and range are tools of descriptive analysis. The considered data is described with the help of such tools. Descriptive statistics also include graphs and charts like histogram, pie chart, bar chart etc. that aids in the visual representation of the data (Kaliyadan & Kulkarni, 2019).

Table 1: Gender of the Participants

Figure 1: Gender of the Participants 

Gender is a categorical or qualitative variable. The descriptive statistics used for understanding such variables are frequency percentages. Table 1 shows the descriptive summary about the gender of the sample under study. Pie chart is used as the visual representative of this data and is shown in Figure 1. Out of the 60 participants, from whom the sample was collected, 38 were females which means majority of the participants were females and only 36.67% of the participating candidates were males. 

Table 2: Origin of the Participants

Figure 2: Origin of the Participants 

By origin, we understand the country were a person is born. Origin is a categorical variable. The frequency and frequency percentage of this variable is stated in Table 2. Here, bar chart is selected as the respective mode of visual representation, which is shown in Figure 2. Only 16.67% of the participants were seen to have not taken birth in Australia, while the rest of the participants were born in Australia. This means majority of the participants have an Australian origin.

Table 3: Living Status of the Participants

Figure 3: Living Status of the Participants 

By Living Status of the participants, we understand whether the people who took part in the survey live alone or not. This is also a categorical variable. A column chart is used in order to visually represent this data. Only 12 people out of the 60 participants are seen to have been living alone and 80% of the participants are seen to have not been living alone i.e. 48 participants live either with family or with a partner.  

Table 4: Religion status of the participants

Figure 4: Religion status of the participants 

The status of religion of the participants mean whether they are affiliated to any religion or not. This data is also categorical. The descriptive summary of the data is shown in Table 4. For the visual representation of this data a stacked column chart is selected, which is shown in Figure 4. The data collected for the study shows that 60% of the participants have agreed to having being affiliated to a religion. On the other hand 24 participants have agreed to the fact that they are atheists i.e. they are not affiliated to any religion.

Exercise and the Mental Health of Youth

Table 5: age of the participants

The average age of the 60 individuals who took part in the survey is 25.49 years, as seen from Table 5. The standard deviation of age is 2.72, which means the variation of the age data is 7.40. The coefficient of variance for the age data is 937.13, which is calculate by dividing the mean of the data by the standard deviation and then multiplying the result with 100. The variance as well as the coefficient of variation is very high for the age data. This means that the data is highly dispersed around the mean.

Table 6: Descriptive Summary

The descriptive summary for the data is stated in Table 6. From the table, it can be seen that, on an average, the participants run 13.75 times a week. The high variance for this data indicates the existence of a large dispersion around the mean. On the other hand the standard deviation and the variance for negative mood is seen to be very low, which means that the spread of this particular data around the mean is quite low. The average score for positive mood of the participants is 6.72.

A correlation analysis is very helpful in understanding the degree of association between two variables. The quantity that is used to understand this association is termed as correlation coefficient. Pearson’s Product Moment Correlation Coefficient and Spearman’s Rank Correlation Coefficient are the two primarily used correlation coefficients. Pearson’s correlation coefficient, denoted by R or r, is a quantity that measures the strength of the linear relationship that exists between two variables. The coefficient of determination, obtained by squaring the correlation coefficients helps in understanding how much one variable impacts another variable (Senthilnathan, 2019).

The hypothesis involved in correlation analysis is:

H₀: there is no correlation between two variables

H₁: there is a significant correlation between two variables.

H₀ is the null hypothesis and H₁ is the alternative or research hypothesis. The null hypothesis gets rejected when the p-value of the correlation analysis is obtained to be less than the considered level of significance.

Table 7: Correlation Table

The correlation coefficients between run and mood, both positive and negative, are significant at 99% significance level. This means the p-value for both the cases are less than 0.01. The correlation coefficient between Run and positive mood is positive. The strength of association between these two variables is moderate. The positive relationship indicates that as the number of runs per week increases the score for positive mood also increases. R-squared value or coefficient of determination between run and positive mood is 0.29. This indicates that id number of runs is used to predict positive mood, it will predict the dependent variable with an accuracy of 29%. The correlation coefficient between Run and negative mood is observed to be negative. The strength of association between these two variables is very high. The negative relationship indicates that as the number of runs per week increases the score for negative mood decreases. R-squared value or coefficient of determination between run and positive mood is 0.74. The p-value already indicates that the number of runs can predict negative mood. The predictive model will be able to determine 74% variation seen in the negative mood score.

Duration of Physical Activity on Mood

The aim of this paper was to understand the level of association between physical exercise and mental health. In this paper, running for at least 30 minutes a week is considered to be the physical exercise. The mental health was defined with the help of positive and negative mood scores of the participants. A survey was conducted through social media sites like Facebook and Twitter. A questionnaire was circulated through the online medium containing questions about the number of times a person runs in a week. The participants were made aware of the fact that the runs that would be considered qualifying would be the ones ran for a minimum of 30 minutes. Positive and negative mood of the participants were asked to score on a scale of 0 to 9. The questionnaire survey also contained questions regarding the demographic profile like age, gender, living status, origin and religion affiliation.  60 samples were collected, in total, for the conducting the respective analysis.

Descriptive analysis was conducted on the demographic profile of the participants. Results obtained from the descriptive analysis showed that the number of female participants was much more than the number of male participants. Frequency percentage of origin was also seen to have a huge difference. The number of participants with an Australian origin was seen to be very high than the number of participants that were not of an Australian origin. A minority of the participants are seen to be living alone. Although, not much difference was observed in the frequency of participants with and without religion affiliations, the frequency for individuals with religion affiliations was seen to be slightly higher than individuals with no religion affiliations.

Correlation Analysis conducted between mood and run showed significant association between the variables (p-value < 0.01). The primary aim of this study that physical exercise has a significant impact on mental health can also be validated with the help of other research works. A study conducted by Chekroud et al, (2018), showed that the people who had the tendency of exercising experienced fewer days of poor mental health than the people who did not have the tendency of exercising. This study considers different types of exercises. The result of the analysis of this study stated that a low level of mental health burden was observed for all types of exercises (minimum reduction of 11·8% and maximum reduction of 22·3%). Exercises involving team sports, cycling and aerobic and gym activities reported highest level of associations. Team sports reported a reduction of 22.3% mental health burden, aerobic and gym activities reported a reduction of 20.1% mental health burden, cycling reported a reduction of 21.6% mental health burden. Another study by Pascoe et al, (2020) states that the reduction of mental depression was seen to be associated with exercise interventions. According to a study conducted by Grandalsmoen et al, (2020), the relationship between all types of mental health problems and physical exercise is found to be negative. Women who have low levels of exercise regularly reported of being depressed, which was not the case for women who exercised regularly. The duration of exercise was also considered in this study. A significant level of association between duration of exercise and mental health problems was observed. Therefore, from the current study and previously conducted studies it can be safely concluded that physical exercise tend to have a significant and positive impact on the mental health of individuals.

Reference

Bhardwaj, P. (2019). Types of sampling in research. Journal of the Practice of Cardiovascular Sciences, 5(3), 157.

Chekroud, S. R., Gueorguieva, R., Zheutlin, A. B., Paulus, M., Krumholz, H. M., Krystal, J. H., & Chekroud, A. M. (2018). Association between physical exercise and mental health in 1· 2 million individuals in the USA between 2011 and 2015: a cross-sectional study. The Lancet Psychiatry, 5(9), 739-746.

Grasdalsmoen, M., Eriksen, H. R., Lønning, K. J., & Sivertsen, B. (2020). Physical exercise, mental health problems, and suicide attempts in university students. BMC psychiatry, 20(1), 1-11.

Kaliyadan, F., & Kulkarni, V. (2019). Types of variables, descriptive statistics, and sample size. Indian dermatology online journal, 10(1), 82.

Parry, D. A., Oeppen, R. S., Amin, M. S. A., & Brennan, P. A. (2018). Could exercise improve mental health and cognitive skills for surgeons and other healthcare professionals?. British Journal of Oral and Maxillofacial Surgery, 56(5), 367-370.

Pascoe, M., Bailey, A. P., Craike, M., Carter, T., Patten, R., Stepto, N., & Parker, A. (2020). Physical activity and exercise in youth mental health promotion: A scoping review. BMJ open sport & exercise medicine, 6(1), e000677.

Senthilnathan, S. (2019). Usefulness of correlation Analysis. Available at SSRN 3416918.

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