The association between mental health and participatory sports consumption: A cross-sectional study

doi:10.21203/rs.3.rs-3458454/v1

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Research Article

The association between mental health and participatory sports consumption: A cross-sectional study

https://doi.org/10.21203/rs.3.rs-3458454/v1

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Background

Participatory sports consumption (PSC) can improve mental wellness. However, despite advances in science, technology, and medicine, evidence suggests that our mental health has declined in recent decades. PSC, which is becoming more prevalent in the sports industry, may enhance our sports experience and positively impact mental health. Therefore, studying the relationship between PSC and mental health is important.

Methods

This study employed the Korea Health Panel Annual Data 2019 data of 11,268 participants aged over 19 years old to evaluate the relationship between PSC and mental health. Adjustments were made for sex, age, education level, household income, and chronic disease. Subgroup analyses were also conducted based on sex, age, and exercise level.

Results

Significant differences between PSC and various factors, including sex, age, education level, household income level, number of chronic diseases, depression, anxiety, suicidal thoughts, poor self-perceived health status, and regular exercise. After controlling for sex, age, household income level, and education level, the presence or absence of PSC had a significant association on stress, anxiety, suicidal thoughts, and poor self-perceived health. A subgroup analysis showed that the impact of participating in sports consumption on stress was significantly higher for females, older adults, and regular exercisers. Additionally, older adults showed a significant difference in the impact of PSC on anxiety and self-perceived health status compared to other age groups.

Conclusions

PSC is associated with improvement in mental health, including those with chronic conditions, and older adults experience greater benefits from PSC than younger individuals.

participatory sports consumption

mental health

The World Health Organization (WHO), defines health as a state of complete physical, mental, and social well-being and not merely the absence of disease or infirmity [1]. Although this idealized concept was presented about 70 years ago, it is noteworthy that it focuses on the multidimensionality of health. Subsequently, the WHO proposed the concept of "No health without mental health," further emphasizing the crucial role that mental health plays in both individual and societal well-being. Mental health is a crucial component of human life impacting not only our psychological well-being but also our quality of life and sense of happiness [2]. As modern society rapidly progresses, an increasing number of individuals are facing various forms of stress and distress, exacerbating the prominence of mental health issues [3]. In the past few decades, there has been a staggering increase in "deaths of despair" in both the United Kingdom [4] and the United States [5]. To make matters worse, the significant pre-existing decline in mental health was greatly exacerbated by the COVID-19 pandemic [6]. Mental wellness may serve as a protective factor in aging and the development of chronic physical conditions [7]. Conversely, mental disorders increase the risk of both communicable and non-communicable diseases and contribute to both accidental and intentional injury [8]. Given the significance of mental health, it is important to identify all potential factors that may intervene in and impact mental well-being.

Participatory sports consumption (PSC), as a form of substantial consumption, is a practice in which consumers use sports participation and time expenditure as a prerequisite to functionally liberate sports consumption resources and meet their consumption needs [9]. PSC is a type of sports consumption and also the foundation and core of the sports industry [10, 11] and serves as the intersection of sports participation and sports consumption. The two necessary conditions for PSC are time and consumption [12]. Participating in sports activities has always been a key way to improve physical and mental health [13], and even appears as a treatment for many physical and mental illnesses [14]. PSC in sports activities not only provides a better environment, more professional guidance, a more comfortable experience, and higher exercise efficiency during the sports activities but also social support and identity recognition through interaction and communication during the consumption process, thus positively impacting mental health [15]. Satisfying individuals' desires for consumption can enhance their subjective sense of happiness and effectively relieve stress and other negative emotions generated by work, study, and life [16]. Although there is a close relationship between PSC and health, due to the fact that PSC is a concept in sports economics, the concept is currently more prevalent in economic research and rarely appears in medical-related research fields.

This study is a cross-sectional study based on the Korean Health Panel Survey (KHPS) [17] survey data. It investigates the relationship between the presence or absence of PSC among individuals of different ages, genders, and exercise habits and five indicators of psychological health: stress, depression, anxiety, suicidal thoughts, and subjective health status. The results provide valuable insights into the potential impact of PSC on the psychological health of different individuals and contribute to the effective data support for improving public mental health. Additionally, the study results can play an important role in increasing sports consumption and promoting the development of the sports industry.

Research subjects

This study utilizes KHPS data 2019 data from the Korea Institute for Health and Social Affairs & National Health Insurance Service (2020), which is a large-scale health survey conducted by the National Statistical Office of South Korea with the aim of understanding the health status and health behaviors of the Korean population. The survey covers various aspects including health status, medical utilization, lifestyle, and environmental factors, among others. The KHPS 2019 data were collected using a two-stage stratified cluster sampling method based on the 2016 registration census to ensure representativeness. The target population included 8,500 households nationwide and their household members, and a minimum sample size of around 500 households per province/autonomous city was determined to derive statistics on medical utilization and medical expenses by province/autonomous city with a maximum allowable error of approximately 4.5% at a 95% confidence level. The sample for this study consisted of 14,741 participants, of which 11,268 aged 19 years or older were included in the analysis after excluding responses with missing or abnormal values for variables related to chronic diseases, mental health, physical activity, and the covariates. The use of data in this study was approved through IRB review by Bioethics Committee of Korea Institute For Health and Social Affairs (KIHASA) (Assignment No. 22-017-00)

Research variables

Mental health

For the survey question "How much stress do you generally experience in your daily life?" there are four answers: 1) I feel a lot of stress, 2) I tend to feel a lot of stress, 3) I tend to feel some stress, and 4) I barely feel any stress. According to the participant's response, stress levels were classified as "YES" (I feel a lot of stress, I tend to feel a lot of stress) or" NO" (I tend to feel some stress, I barely feel any stress). Participants were asked binary questions regarding melancholy feelings, feelings of insecurity, and suicidal thoughts: "Have you ever felt so sad or unhappy that it interfered with your daily life for two or more consecutive weeks in the past year?"; "Have you experienced excessive anxiety or worry that has impacted your daily life for at least six months continuously in the past year?"; and "Have you thought about dying in the past year?" Based on the answers, participants were categorized as "YES" or "NO". Poor self-perceived health status was also divided into two categories: "YES" for feeling poor or very poor and "NO" for feeling very good, good, or moderate.

Participatory sports consumption

Participants who responded "YES" to the question " Did you spend any money to participate in sports activities in the last year?" were classified as having PSC.

Consistent regular exercise

Regarding exercise habits, participants were asked, "Did you regularly engage in any physical activity or exercise (including walking) in the past year?" Participants who answer "YES" were considered to have a liking for consistent regular exercise.

Covariates

The general demographic variables included sex, age, educational level, and household income. Sex was dichotomized into male and female. Age was divided into four groups: 19–29, 30–49, 50–65, and ≥ 66. Education level was categorized into three levels: middle school or below, high school, and college or above. Household income was calculated as the quartile range of equalized total household assets, ordinary and non-recurring income, and wages and business income of family members based on annual data.

As covariates, the study also considered a number of chronic diseases, including hypertension, diabetes, cardiovascular disease, cerebrovascular disease, joint disease, spinal disease, cancer, hepatitis, respiratory disease, thyroid disease, and obesity. Cardiovascular disease included angina pectoris and myocardial infarction; cerebrovascular disease included cerebral hemorrhage and cerebral infarction; joint disease included gonarthrocace, degenerative arthritis of joints outside the knee, and rheumatoid arthritis; spinal disease included intervertebral disc disorders (e.g., herniated discs and spinal stenosis) and other spinal diseases; cancer included stomach, colon, lung, breast, cervical, thyroid, and other cancers; hepatitis included chronic hepatitis (types B and C), alcoholic hepatitis, and liver cirrhosis; respiratory disease included asthma, emphysema, chronic obstructive pulmonary disease (COPD), and bronchial dilatation; thyroid disease included hypothyroidism and hyperthyroidism; and obesity was defined as a body mass index (BMI) value greater than 25, calculated as weight divided by height squared.

Statistical analysis

Statistical processing and analysis of the data was conducted using IBM SPSS Statistics 26.0 and Origin 2021 software, and a p-value less than 0.05 was considered statistically significant. Continuous variables were represented by the mean and standard deviation, and categorical variables were represented by frequency count and percentage (%). Chi-square and t-tests were performed to analyze the participants’ general characteristics based on PSC. We divided all research participants into four groups based on whether they participated in PSC and whether they exercised regularly. Then we performed multiple comparisons and used the Bonferroni correction to control the overall significance level for the chi-squared test results between the four groups. Specifically, we set the significance level to α/m, where α is the predetermined significance level and m is the number of multiple comparisons. By applying the Bonferroni correction, we reduced the significance level for each comparison to α/m to control the error rate of the overall significance level resulting from multiple comparisons. Binary logistic regression analysis was used to determine the relationship between PSC and mental health, and the odds ratio and 95% confidence interval were analyzed. Model 2 was adjusted for sex, age, educational level, and household income, and Model 3 was adjusted for chronic disease to improve the accuracy of the regression model. Based on the regression analysis between PSC and mental health, subgroup analysis was performed to compare the effects of PSC on mental health under different conditions of sex, age, and exercise habits, and interaction item analysis was performed to determine whether there was a significant difference between subgroups.

Table 1

Demographic characteristics
Characteristics		N (%)
Sex
	Male	4993 (44.3)
	Female	6275 (55.7)
Age
	Mean ± SD	56 ± 16
Education level
	Middle school or below	4048 (35.9)
	High school	3427 (40.4)
	College or above	3793 (33.7)
Household income
	Mean ± SD (usd/year)	34700 ± 28431
Total		11268

Table 1 presents the descriptive statistics of participants’ demographic characteristics. A total of 11,268 participants were included in the study, with 4,993 (44.3%) males and 6,275 females (55.7%). The mean age was 56 years, with an evenly distributed education level: middle school or below (35.9%), high school (40.4%), and college or above (33.7%). The mean household income was 34,700 ± 28,431 USD per year.

Table 2

Factors related to PSC
Factors		Participation in PSC N (%)		p-value
Factors		no	yes	p-value
Sex
	Male	4143 (43.4)	850 (49.7)	0.000
	Female	5414 (56.6)	861 (50.3)
Age
	19 ~ 29	570 (6.0)	236 (13.8)	0.000
	30 ~ 49	2273 (23.8)	721 (42.1)
	50 ~ 65	3111 (32.6)	524 (30.6)
	≥ 66	3603 (37.7)	230 (13.4)
	Mean ± SD	58 ± 16	47 ± 15
Education level
	Middle school or below	3888 (40.7)	160 (9.4)	0.000
	High school	2975 (31.1)	452 (26.4)
	College or above	2694 (28.2)	1099 (64.2)
Household income level
	Low-Income Group	2703 (28.3)	163 (9.5)	0.000
	Lower-Middle-Income Group	2552 (26.7)	270 (15.8)
	Upper-Middle-Income Group	2318 (24.3)	480 (28.1)
	High-Income Group	1984 (20.8)	798 (46.6)
	Mean ± SD (usd/year)	4031 ± 3263	6569 ± 4550	0.000
Number of chronic diseases
	0	3298 (34.5)	863 (50.4)	0.000
	1	2708 (28.3)	525 (30.7)
	≥ 2	3551 (37.2)	323 (18.9)
High stress
	No	6736 (70.5)	1230 (71.9)	0.239
	Yes	2821 (29.5)	481 (28.1)
Depression
	No	8740 (91.5)	1609 (94.0)	0.000
	Yes	817 (8.5)	102 (6.0)
Anxious
	No	9002 (94.2)	1656 (96.8)	0.000
	Yes	555 (5.8)	55 (3.2)
Thoughts of suicide
	No	8789 (92.0)	1646 (96.2)	0.000
	Yes	768 (8.0)	65 (3.8)
Poor self-perceived health
	No	7700 (80.6)	1576 (92.1)	0.000
	Yes	1857 (19.4)	135 (7.9)
Regular exercise
	No	4936 (51.6)	391 (22.9)	0.000
	Yes	4621 (48.4)	1320 (77.1)
Total		9557	1711

Table 2 evaluated the relationship between various factors and PSC. It showed that factors such as sex, age, educational level, household income, number of chronic diseases, mental health were found to be related to PSC. Overall, male, age between 30–49 and 50–65, without chronic diseases, higher educated and higher-income demonstrated were more likely to PSC. In terms of mental health, depression, anxious, thoughts of suicide, and poor self-perceived health status all showed significant differences in relation to PSC. Furthermore, it was found that 22.9% of individuals who engage in PSC did not engage in regular exercise, which is highly relevant for further research.

Figure 1 shows the NP group and PP groups indicated significantly higher stress levels than the RP and RE groups. The NP group had significantly higher levels of depression than groups RP and RE, while there was no significant difference between the PP group and the other groups. In With regard to anxiety, the NP had significantly higher levels than the RE group, the RE group had significantly higher levels than the RP, and there was no significant difference between the P group and the other three groups. Individuals in the NP group had a significantly higher frequency of suicidal thoughts compared to individuals in the PP and RP groups, and the RE group also showed a significantly higher frequency of suicidal thoughts compared to the RP group. Regarding the statistical data on poor self-perceived health, the NP group showed a significantly higher frequency compared to the RE group, and the RE group had a significantly higher frequency compared to the PP and RP groups. However, there was no significant differences between the PP and RP groups.

Table 3

Relationship between mental health and PSC
Factors	Comparison between no PSC and PSC (Ref)
	Model one^a	Model two^b	Model three^c
	OR (95% CI)	OR (95% CI)	OR (95% CI)
High stress	1.07 (0.96–1.20)	1.27 (1.12–1.43)	1.28 (1.13–1.45)
Depression	1.47 (1.19–1.82)	1.21 (0.97–1.51)	1.21 (0.97–1.51)
Anxious	1.86 (1.40–2.46)	1.42 (1.06–1.91)	1.42 (1.06–1.90)
Thoughts of suicide	2.21 (1.71–2.87)	1.68 (1.28–2.19)	1.68 (1.28–2.20)
Poor self-perceived health	2.82 (2.34–3.38)	1.47 (1.21–1.79)	1.49 (1.22–1.81)
a: Model one unadjusted OR (95% CI). b: Model two adjusted for gender, age, household income level, and education level. c: adjusted for covariates in Model two and chronic diseases

Table 3 shows the relationship between mental health and PSC. The binary logistic regression analysis of Model 1 showed that compared to those who engage in PSC, those who do not were more likely to experience depression (1.47, 95% CI: 1.19–1.82), anxiety (1.86, 95% CI: 1.40–2.46), suicidal thoughts (2.21, 95% CI: 1.71–2.87), and poor self-perceived health status (2.82, 95% CI: 2.34–3.38), but there was no significant difference in terms of stress. Model 2, after adjusting for sex, age, education level, and household income, showed that PSC was related to stress (1.27, 95% CI: 1.12–1.43), anxiety (1.42, 95% CI: 1.06–1.91), suicidal thoughts (1.68, 95% CI: 1.28–2.19), and poor self-perceived health status (1.47, 95% CI: 1.21–1.79). Model 3, after adjusting for chronic diseases, still showed that PSC was related to stress (1.28, 95% CI: 1.13–1.45), anxiety (1.42, 95% CI: 1.06–1.90), suicidal thoughts (1.68, 95% CI: 1.28–2.20), and poor self-perceived health status (1.49, 95% CI: 1.22–1.81), indicating that the relationship between PSC and mental health is not affected by the presence or absence of chronic diseases.

Figure. 2–5 shows the results of the subgroup analysis of the regression model, which demonstrates the impact of participating in PSC on stress (Fig. 2), anxiety (Fig. 3), suicidal thoughts (Fig. 4), and poor self-perceived health (Fig. 5) among different subgroups defined by gender, age, and regular exercise habits. The significance of the interaction terms in the regression model was used to determine whether there were significant differences between three subgroups.

The impact of PSC on feeling stressed was significantly higher for women (1.52, 95% CI: 1.28–1.81), older adults (1.70, 95% CI: 1.14–2.53), and regular exercisers (1.18, 95% CI: 1.01–1.38) compared to other subgroups. (Fig. 2)

The impact of PSC on anxiety was significantly higher for older adults (2.81, 95% CI: 1.13–6.96) compared to other subgroups, with no significant differences by sex (p = 0.825) and regular exercise (p = 0.174). (Fig. 3)

The impact of PSC on suicidal thoughts showed no significant differences by sex (p = 0.394), age (p = 0.963), and regular exercise (p = 0.259). (Fig. 4)

The impact of PSC on poor self-perceived health status was significantly higher for older adults (2.26, 95% CI: 1.48–3.44) compared to other subgroups, with no significant differences by sex (p = 0.617) and regular exercise (p = 0.565). (Fig. 5)

Our research results indicate that individuals who engage significantly associated with lower levels of suicidal thoughts and poor self-perceived health compared to those who do not engage in PSC. Furthermore, our findings suggest that among the individuals who engage in PSC, having a regular exercise habit does not provide further improvement in terms of suicidal thoughts and poor self-perceived health. These findings highlight the potential benefits of PSC for mental and physical health and provide insights into the limitations of exercise alone in improving these outcomes. Here, we can interpret our findings from two perspectives. First, a positive and healthy psychological state can facilitate individuals to adopt a healthy lifestyle guided by exercise [18]. This positive state reinforces the subjective need for exercise and enhances the willingness to consume exercise-related products and services. On the other hand, consumption itself can also be viewed as a positive mental health behavior, particularly in the case of PSC, where consumers are required to actively participate and spend a significant amount of time engaged in the activity. This process of consumption may enhance an individual's self-esteem and sense of fulfillment [19]; for instance, imagine joining an excellent fitness club as a member or purchasing a pair of long-awaited basketball shoes.

Our subsequent regression analysis model provided additional evidence of the impact of PSC on mental health. After controlling for age, household income, gender, and education level, we found that PSC remained a significant factor in reducing negative psychological states such as high stress, depression, suicidal thoughts, and poor self-perceived health. David Ricardo proposed that the conditions necessary for the emergence of consumption include demand, purchasing power, product supply, marketing, and social culture. These factors interact and influence each other, and the relative importance of different conditions varies depending on the situation [20, 21]. For example, in a situation where product supply is abundant, marketing may play a more significant role, whereas in areas with relatively low-income levels, purchasing power may be a crucial factor limiting consumer behavior. No single condition can independently determine the emergence of consumer behavior, and each condition has a certain level of importance. Therefore, we have reason to believe that when people begin to demand physical activity in the presence of all the objective factors of PSC, they may already start benefiting from sports.

Developed countries, such as South Korea, have relatively complete industrial chains and sufficient production materials, so both the individual's subjective demand for sports and the level of sports industry development in the region largely depend on the degree of influence of sports culture. Through the dissemination and popularization of sports culture, people's attention to sports events and physical health continues to increase, thereby promoting the development of sports consumption.

Through comparing the regression results of Models 2 and 3, we found that the actual health status of individuals has little impact on the regression results, indicating that the effect of PSC on mental health is applicable to both completely healthy individuals and individuals with chronic diseases. Therefore, we believe that improving the mental health of chronic disease patients through increasing PSC is theoretically feasible. Regarding the question of whether there is a difference in psychological well-being between individuals with chronic diseases and the general population, research findings among different scholars show heterogeneity. Some studies have indicated that individuals with various medical conditions, including arthritis, diabetes, cancer, renal disease, and dermatologic disorders, do not differ significantly in subscale or overall mental health scores from other medical groups or the general population [22]. Furthermore, Mason found no significant differences in anxiety and depressive symptoms between patients with rheumatoid arthritis, diabetes, pulmonary disease, hypertension, cancer, and cardiac disease [23]. Despite these findings, there are also many studies that have shown that individuals with chronic illnesses are more susceptible to various mental health problems [24, 25]. However, regardless of whether there is a significant difference in the psychological well-being of individuals with chronic conditions compared to the general population, it is indisputable that a healthy psychological state is a protective factor against chronic physical conditions and aging7. Moreover, an unhealthy psychological state is one of the contributing factors to certain chronic diseases [26, 27].

The psychological health status of older adults has always been a widely-discussed topic in the field of health. Despite declining physical functions in various aspects, the old age period is characterized by increased subjective well-being [28]. The severity and fatality of COVID-19 are closely linked to age and compromised immune states, leading to higher infection and mortality rates among elderly individuals compared to younger individuals during the pandemic [29]. In response to this, the WHO has recommended strict social isolation measures for the older adult population to control the high death tolls in heavily affected countries, as outlined in its guidelines. As vulnerability increases among older adults, there has also been a concurrent rise in fear, panic, and apprehension among both seniors and their families [30]. While social distancing has been a key strategy in combating COVID-19 [31], it has also been identified as a major contributor to loneliness, especially in vulnerable settings such as nursing homes or assisted living facilities. This isolation has been identified as an independent risk factor for depression, anxiety disorders, and suicide among older adults [32]. In our study, the results of the subgroup regression model showed that stress, depression, and subjective health status among older individuals were more affected by participation in sports consumption. Additionally, the interaction term test indicated that this difference was statistically significant. Furthermore, these results were obtained while controlling for gender, income, and education level. Although our results showed that older adults can benefit more in terms of mental health by participating in PSC compared to younger adults, we also found that the main targets of PSC are still young people. In our study population, those aged 65 years and older accounted for 37.7% of the total population but only 13.4% of those engaged in PSC, which is significantly lower than the other age groups. Similar findings have been reported in other countries where relevant studies have been conducted [33]. Older adults may face more barriers to participating in sports compared to younger individuals, which could be one of the reasons for their lower engagement in sports-related consumption. For instance, older adults may have concerns about injury or health problems and worry that their abilities may not meet the demands of sports participation, which may impede their involvement [34]. Moreover, older adults may be less susceptible to advertising or social media influence, which may make them more conservative in their consumption decision-making [35]. To increase the frequency of older adults' participation in sports consumption, efforts are needed in various aspects. In terms of marketing strategies, due to the cognitive and thinking differences of older adults, there is a need for more targeted marketing strategies to meet their needs in purchasing decisions [36]. Governments should also implement targeted public policy interventions for older adults, such as community and home-based health services, exercise and physical activity programs, building and public space environmental design, and informational and educational interventions [37].

Participatory sports consumption is a critical factor that affects individual mental health, regardless of whether the individual is healthy or a chronic disease sufferer. Moreover, compared to their younger counterparts, older adults exhibit more pronounced improvement in their mental health through PSC. The wide demographic reach and low-cost nature of sports activities make it a feasible strategy to enhance the mental health of the general population, particularly among older adults. Therefore, promoting PSC could be an effective tool for improving mental health and should be considered a priority in public health policy.

WHO, World Health Organization; PSC, Participatory sports consumption; KHPS, Korean Health Panel Survey; KIHASA, Korea Institute For Health and Social Affairs; COPD, Chronic obstructive pulmonary disease; BMI, Body mass index.

Ethics Approval and Consent to Participate

All participants in this study provided informed consent before their involvement. The research was conducted with the ethical approval of the Research Ethics Review Committee at the Korea Institute for Health and Social Affairs, under Approval Number 22-017-00.

Consent for Publication

Not applicable.

Availability of Data and Material

The data supporting the findings of this study can be obtained from the Korea Health Panel survey. The availability of these data is subject to restrictions, as they were used under license for the current research and are not publicly available. However, data can be accessed from the Korea Health Panel upon reasonable request and submission of an application.

Competing interests

Not applicable.

Funding

Not applicable.

Author Contributions

Yaqi Su conducted and managed all aspects of the research. Yaqi Su is the sole contributor to this study.

Acknowledgments

Not applicable.

World Health Organization. WHO remains firmly committed to the principles set out in the preamble to the Constitution [Internet]. World Health Organization. 1948. Available from: https://www.who.int/about/governance/constitution
Ryff CD, Singer B. Psychological Well-Being: Meaning, Measurement, and Implications for Psychotherapy Research. Psychotherapy and Psychosomatics. 1996;65(1):14–23.
Wadhwa S. Stress in the modern world : understanding science and society. Santa Barbara, California: Greenwood, An Imprint Of Abc-Clio, Llc; 2017.
Augarde E, Gunnell D, Mars B, Hickman M. An ecological study of temporal trends in “deaths of despair” in England and Wales. Social Psychiatry and Psychiatric Epidemiology. 2022 Mar 5;
Tilstra AM, Simon DH, Masters RK. Trends in “Deaths of Despair” among Working Aged White and Black Americans, 1990-2017. American Journal of Epidemiology. 2021 Mar 29;
Mental Health and COVID-19: Early evidence of the pandemic’s impact: Scientific brief, 2 March 2022 [Internet]. www.who.int. Available from: https://www.who.int/publications-detail-redirect/WHO-2019-nCoV-Sci_Brief-Mental_health-2022.1
Keyes CLM. Chronic physical conditions and aging: Is mental health a potential protective factor? Ageing International. 2005 Dec;30(1):88–104.
Prince M, Patel V, Saxena S, Maj M, Maselko J, Phillips MR, et al. No health without mental health. The Lancet. 2007 Sep;370(9590):859–77.
DAI G, QIU J. Review on the Research Paradigms,Content Analysis and Characters of Sports Consumption. Journal of Tianjin University of Sport. 2009 Nov 25;(6):494–8.
Humphreys BR, Ruseski JE. Estimates of the Dimensions of the Sports Market in the US. 2009 May 1;4(2):94–113.
Silva A, Monteiro D, Sobreiro P. Effects of sports participation and the perceived value of elite sport on subjective well-being. Sport in Society. 2019 May 23;1–22.
MA T, LU X. Time,Income Effect and Participatory Sport Consumption. Journal of Shanghai University of Sport. 2022 May 15;46(6):85–96.
Chutima Jalayondeja, Wattana Jalayondeja, Jattuporn Suttiwong, Sullivan PE, Deepika L H K Nilanthi. PHYSICAL ACTIVITY, SELF-ESTEEM, AND QUALITY OF LIFE AMONG PEOPLE WITH PHYSICAL DISABILITY. Southeast Asian Journal of Tropical Medical Public Health. 2016 May 1;47(3):546–58.
Sallis RE. Exercise is medicine and physicians need to prescribe it! British Journal of Sports Medicine. 2009 Nov 19;43(1):3–4.
Zhigang W, Kai G, Chao W, Hongyan D, Lei Z, Zhao X. How can social needs impact on meaningful sports consumption? Frontiers in Psychology. 2022 Nov 3;13:1043080.
Su T. Consumption Pattern and Mental Health of Employees Based on Big Data Analysis. Gupta A, editor. Journal of Environmental and Public Health. 2022 Jun 28;2022:1–7.
Korea Institute for Health and Social Affairs, National Health Insurance Service. Korea Health Panel Annual Data 2019. 2020. https://www.khp.re.kr
Peluso MAM, Andrade LHSG de. Physical Activity and Mental health: the Association between Exercise and Mood. Clinics [Internet]. 2005 Feb;60(1):61–70. Available from: https://www.sciencedirect.com/science/article/pii/S1807593222032173
Ni S, Ishii K. The influence of shopping on subjective well-being in China. Asia Pacific Journal of Marketing and Logistics. 2019 Jan 14;31(1):184–201.
Armstrong G, Adam S, Denize S, Kotler P. Principles of Marketing VS. Sydney: Pearson Education Australia; 2014.
Sheth JN, Newman BI, Gross BL. Consumption Values and Market Choices: Theory and Applications. South-Western Pub. 1991;
Cassileth BR, Lusk EJ, Strouse TB, Miller DS, Brown LL, Cross PA, et al. Psychosocial Status in Chronic Illness. New England Journal of Medicine. 1984 Aug 23;311(8):506–11.
Mason JH, Weener Jl, Gertman PM, Meenan RF. Health status in chronic disease: a comparative study of rheumatoid arthritis. 1983 Oct 1;10(5):763–8.
Rugulies R. Depression as a predictor for coronary heart disease. American Journal of Preventive Medicine. 2002 Jul;23(1):51–61.
Cosgrove MP, Sargeant LA, Griffin SJ. Does depression increase the risk of developing type 2 diabetes? Occupational Medicine. 2008 Jan 1;58(1):7–14.
Black PH. Stress and the inflammatory response: A review of neurogenic inflammation. Brain, Behavior, and Immunity [Internet]. 2002 Dec 1;16(6):622–53. Available from: https://www.sciencedirect.com/science/article/abs/pii/S0889159102000211
Brindley DN, McCann BS, Niaura R, Stoney CM, Suarez EC. Stress and lipoprotein metabolism: Modulators and mechanisms. Metabolism. 1993 Sep;42(9):3–15.
Steptoe A, Deaton A, Stone AA. Subjective wellbeing, health, and ageing. The Lancet [Internet]. 2015 Feb;385(9968):640–8. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4339610/
MEO SA, ALHOWIKAN AM, AL-KHLAIW T, MEO IM, HALEPOTO DM, IQBAL M, et al. Novel coronavirus 2019-nCoV: prevalence, biological and clinical characteristics comparison with SARS-CoV and MERS-CoV . European Review for Medical and Pharmacological Sciences. 2020;24(4).
Banerjee D. The Impact of Covid‐19 Pandemic on Elderly Mental Health. International Journal of Geriatric Psychiatry [Internet]. 2020 May 4;35(12). Available from: https://onlinelibrary.wiley.com/doi/10.1002/gps.5320
Milne GJ, Xie S. The Effectiveness of Social Distancing in Mitigating COVID-19 Spread: a modelling analysis. MedRxiv. 2020 Mar 23;
Luchetti M, Lee JH, Aschwanden D, Sesker A, Strickhouser JE, Terracciano A, et al. The trajectory of loneliness in response to COVID-19. American Psychologist. 2020 Jun 22;75(7).
YANG F, PAN Y, ZOU Z. Patterns and determinants of physical activity of elderly people In China. China Sport Science and Technology. 2019 Aug 20;55(10):10–21.
SCHUTZER K. Barriers and motivations to exercise in older adults. Preventive Medicine. 2004 Nov;39(5):1056–61.
Gilly MC, Zeithaml VA. The Elderly Consumer and Adoption of Technologies. Journal of Consumer Research. 1985 Dec;12(3):353.
Yoon C, Laurent G, Fung HH, Gonzalez R, Gutchess AH, Hedden T, et al. Cognition, Persuasion and Decision Making in Older Consumers. Marketing Letters. 2005 Dec;16(3-4):429–41.
Grande GD, Oliveira CB, Morelhão PK, Sherrington C, Tiedemann A, Pinto RZ, et al. Interventions Promoting Physical Activity Among Older Adults: A Systematic Review and Meta-Analysis. Heyn PC, editor. The Gerontologist. 2019 Dec 23;

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The association between mental health and participatory sports consumption: A cross-sectional study

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BACKGROUND

METHODS

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