Effects of Mood Regulation on Sociodemographic Status, Exercise Pattern, and Physical Conditions of Chinese Adults and the Elderly

Lou, Jingjing; Li, Tong; Gao, Xuefeng; Hu, Ying; Chen, Xiao; Yang, Fan; Zheng, Xiabing; Yang, Weirui; Zheng, Liangrong; Zhu, Qi; Zhang, Yaqi; Guan, Nianhong

doi:https://doi.org/10.1155/2022/2034957

Computational and Mathematical Methods in Medicine

On this page

Abstract Introduction Methods Results Discussion Conclusion Data Availability Disclosure Conflicts of Interest Authors’ Contributions Acknowledgments References Copyright Related Articles

Special Issue

Mathematical Modelling of IoT-Based Health Monitoring System

View this Special Issue

Research Article | Open Access

Volume 2022 | Article ID 2034957 | https://doi.org/10.1155/2022/2034957

Effects of Mood Regulation on Sociodemographic Status, Exercise Pattern, and Physical Conditions of Chinese Adults and the Elderly

Jingjing Lou,¹Tong Li,²Xuefeng Gao,¹Ying Hu,¹Xiao Chen,¹Fan Yang,³Xiabing Zheng,²Weirui Yang,²Liangrong Zheng,²Qi Zhu,²Yaqi Zhang,²and Nianhong Guan²

Academic Editor: Ahmed Faeq Hussein

Received05 May 2022

Revised06 Jun 2022

Accepted17 Jun 2022

Published04 Jul 2022

Abstract

Objective. This study focused on mood regulations and their association with sociodemographic status, exercise pattern, and physical conditions of adults and older adults in China who did not undergo interventions. Method. Data were based on the 2016 to 2018 Guangdong National Physique Monitoring data, in which 5242 participants aged 20-69 years were recruited. Multiple statistical analysis methods, such as descriptive and logistic regression analyses, were used to study each exercise motivation and its association with influencing factors, including sociodemographic characteristics, exercise measurements, and physical conditions. An exercise index for mental health was also used to investigate the number and types of people who were more likely to meet the index. Results. We observed that 44.9% (2355/5242) of participants did not engage in physical exercise in this study. Only older participants (40 to 69 years old) and those with an average level of education (high school/technical secondary school) showed a significant association with exercising for mood regulation. Few people met the index that is good for mental health (16.64% [872/5242] met index 1, and 2.84% (149/5242) met index 2), and higher education showed a significant association with a reduction in the mental health burden and the prevention of depression. Conclusion. This study found that motivating people to be more active and educating them on the potential mental health benefits of exercise could help them to exercise more.

1. Introduction

Depression is a common mood disorder affecting individuals of all ages and is characterized by persistent sadness, a loss of interest in activities, energy loss, appetite changes, etc. It is reported that more than 300 million people are now living with depression, which increased by more than 18% between 2005 and 2015 [1]. Its morbidity varies by country and region, but patterns and trends for depressive disorders are remarkably similar worldwide [2]. Depressive disorders can occur comorbidly with nearly every other form of psychopathology in an unsystematic way, leading to greater symptom severity and disability, more attempted suicides, and poorer treatment response [3]. Major depressive disorder, accounting for 4.4% of the disease burden worldwide, increased in rank from the 15th to 11th, for which the cost of the traditional pharmacological treatment could reach approximately US $91 billion in 2030 [4, 5]. Depressive symptoms can significantly decrease the quality of life even if an individual does not meet the threshold for a diagnosis of major depressive disorder [6]. However, depression remains widely undiagnosed and untreated because of stigma, a lack of effective therapies, and inadequate mental health resources [7].

Greater depression is associated with physical inactivity, but evidence suggests that exercise has a large and significant antidepressant effect in people with depression symptoms and in those who are diagnosed with depression [8–10]. Meanwhile, with a large portion of patients with depression who do not properly respond to drug treatment (30% response without remission, 20% partial remission, and 50% remission) [11], exercise is an effective, safe, easily accessible, inexpensive form of therapy that requires less clinician training to perform and carries a low risk of negative side effects [12]. It should be added along with standard drug treatment to improve the health status of patients [13, 14]. Converging evidence suggests that exercise and antidepressant medication may alleviate depression through common neuromolecular mechanisms, including increased expression of neurotrophic factors, increased availability of serotonin and norepinephrine, and reduced systemic inflammatory signaling [15]. In a large US cohort, individuals who exercised had fewer days of poor mental health than those who did not exercise [16]. Regular exercise can protect against future risk of depression in individuals of all ages [17, 18], which was confirmed in a meta-analysis sampling from 105 studies, which highlighted the role of regular aerobic exercise in increasing feelings of well-being and suggested that its absence can have detrimental effects on physical and mental health [19].

Although the benefits of exercise are well described, the high rate of inactivity is worrisome. Recently, it was shown that 31% of adults are physically inactive globally, and physical inactivity is responsible for 6% of deaths globally, making it the fourth-leading risk factor for mortality worldwide [20, 21]. The Active People Survey from Sport England in 2015–2016 found that only 36% of adults reported taking part in sports once a week [22]. The percentage of the population who reported never engaging in moderate physical activity was 66% in Nepal and 64.9% in Bangladesh, based on the WHO World Health Survey [23]. Similarly, people with depression had a high rate of physical inactivity [24]. A total of 34.8% of participants with depression were physically inactive according to data from 24,230 people with depression across 46 low- and middle-income countries [25].

The reasons affecting people’s exercise decisions are complex and could include influencing factors such as sociodemographic status, self-efficacy [26], weight loss [27], enjoyment, and physical health [28]. In another review of the salient exercise psychology literature, studies have emphasized that mood regulation depends on interactions among participants, exercise type, and practice conditions and have made several recommendations for structuring an exercise session to maximize mood regulation [29]. Although knowledge of the relationship between exercise and mood regulation is substantial, few studies have investigated the role of specific exercise motivations, especially mood regulation, in relation to sociodemographic status, exercise pattern, and physical condition in the absence of interventions.

This study is aimed at determining the effects of exercises on mood regulation and the reasons affecting peoples’ decision to exercise in a nonintervention condition to provide a theoretical and potentially practical basis for future research on exercise interventions.

2. Methods

2.1. Participants

This study was based on the 2016 to 2018 Guangdong National Physique Monitoring data from the Guangzhou Institute of Sports Science (Guangzhou, China). We measured mental health burden (in this study, mental health burden was considered as a generalized term that could mentally affect the participants due to changes in their physical appearance, behaviors, and daily life if they do not exercise) according to participants’ responses to the following question: “what is the main reason why you perform physical exercise?” The answers could be chosen from the following options: (1) disease prevention and treatment, (2) entertainment, (3) mood regulation, (4) losing weight, (5) bodybuilding, (6) social contact, (7) improving motor skills, (8) increasing physical activity, (9) unclear motivation, and (10) other motivation. To study the factors influencing different exercise motivations, missing data for exercise motivation were excluded. Only data from adults (20–59 years) and senior adults (60–69 years) who had clear exercise motivations were used for this study.

2.2. Anthropometric Measurement

The participants’ height, weight, and abdominal circumference data were obtained from interviewer-administered questionnaires and standardized physical and physiological measurements. Participants were weighed in lightweight clothing without shoes to the nearest 0.1 kg on a calibrated digital scale, and the height was measured to the nearest 0.1 cm with a calibrated stadiometer [30].

Waist circumference measurements were taken horizontally to the nearest 0.1 cm at the midpoint between the inferior margin of the last rib and the iliac crest, with participants standing with feet 25–30 cm apart. Body mass index (BMI) was calculated as weight divided by height squared (kg/m²). The World Health Organization (WHO) BMI cutoff points for Asian populations were used to define underweight (), normal weight (), overweight (), obese I (), and obese II () [31]. The WHO recommended the International Diabetes Federation criteria for ethnic- or country-specific values for waist circumference. Waist circumference cutoff points for Chinese individuals were used to define abdominal obesity ( in men or >80 cm in women) [32].

2.3. Covariate Measurement

Sociodemographic characteristics were assessed using the National Physique Monitoring questionnaire. Sociodemographic variables included gender, age, highest education level, and occupations/jobs. For educational background, the participants were classified as having graduated from primary school or below, middle school, high school/technical secondary school, and college/junior college or above. In regard to participants’ occupations/jobs, they were classified as students, administrative staff and professionals, business service employees, workers, others, and homemakers and retired individuals.

2.4. Measurement of Exercise Characteristics

Exercise characteristics included exercise frequency, exercise duration each time, exercise duration per week, and exercise intensity. All participants were asked “Have you engaged in physical exercise since last year?” The response options were “yes” and “no.” All participants were asked how often they engaged in exercise, with the following seven possible response options: (1) less than once a month, (2) over once a month but less than once a week, (3) once a week, (4) twice a week, (5) three times a week, (6) four times a week, and (7) five times a week or over. Participants were also asked about exercise duration during each exercise session. We estimated exercise duration per week by multiplying exercise frequency by exercise duration each session. Exercise intensity was divided into mild, moderate, and heavy according to changes in respiratory rate, heart rate, and sweating compared to those measured at rest.

2.5. Basic Disease Measurement

For basic diseases, participants were asked “Have you ever been diagnosed with the diseases below by experts or doctors?” The response options included the following: (1) hypertension, (2) hyperlipidemia, (3) hypercholesterolemia, (4) diabetes, (5) coronary heart disease, (6) peptic ulcer, (7) osteoarthropathy, and (8) cervical spondylopathy.

2.6. Exercise Index for Mental Health

Evidence for the optimal exercise duration of each session (45 min) and exercise frequency (between three and five times per week) was reported to reduce participants’ mental health burden [16]. We divided the data into “exercise index for reducing the mental health burden” (exercise 30-60 min each session and 3-5 times per week or over), coded as “meet index 1,” and “exercise index not for reducing the mental health burden” (less than 30 min or more than 60 min per week and less than 3 times per week), coded as “unmet index 1,” according to the recommendation above.

In Samuel’s study of “exercise and the prevention of depression,” it was suggested that assuming the relationship is causal; 12% of future cases of depression could have been prevented if all participants had engaged in at least 1 hour of physical activity each week. According to this recommendation, we divided the data into “exercise index for the prevention of depression” (the exercise motivation is mood regulation, and exercise is performed 60-120 min each week), coded as “meet index 2,” and “exercise index not for the prevention of depression” (the exercise motivation is not mood regulation, or exercise is performed less than 60 min or over 120 min each week), coded as “unmet index 2” [17].

2.7. Statistical Analysis

All analyses were conducted using SAS 9.4 (SAS Institute Inc., Cary, NC). A value of <0.05 was considered significant.

We used descriptive statistics to report the frequency of sociodemographic characteristics (gender, age, highest education level, and job), exercise measurements (exercise frequency, exercise duration each session, exercise duration per week, and exercise intensity), and physical conditions (BMI, abdominal obesity, and basic diseases). Chi-square tests and -tests were used to compare frequencies data. Disordered multiclassification logistic regression models were used to determine if the observed differences between exercise motivations, mood regulation and sociodemographic characteristics, exercise measurements, and physical condition remained significant. Both the likelihood ratio test and the Wald test were used in the study as well.

We used PROC SURVEYFREQ to calculate prevalence and 95% CIs for the eleven categories of exercise motivations as follows: (1) disease prevention and treatment, (2) entertainment, (3) mood regulation, (4) increasing physical activity, (5) weight loss, (6) social contact, (7) disease prevention and treatment and entertainment, (8) disease prevention and treatment and mood regulation, (9) entertainment and mood regulation, (10) whether the participant has met index 1, and (11) whether the participant has met index 2. A series of multiple logistic regression analyses were conducted to examine the associations of the different exercise motivations listed above with sociodemographic characteristics, exercise measurements, and physical conditions. We reported odds ratios (ORs) with corresponding 95% confidence intervals (95% CIs).

2.8. Ethics

The study protocol was approved by the ethical review committee of Guangzhou Institute of Sports Science, Administration of Sports of Guangzhou Municipality. Written informed consent was obtained from each participant at enrollment.

3. Results

As shown in Table 1, a total of 5242 participants were involved in this study (47.3% male and 52.7% female). The participants had a high physical inactivity rate of 44.9% (2355/5242). Logistic regression analyses of sociodemographic characteristics, exercise measurements, and physical conditions are shown in Table 2. There were different patterns of characteristics within these 9 categories of exercise motivations.

Regarding the sociodemographic characteristic of gender, there was no significant difference between gender and the exercise motivations of mood regulation, increasing physical activity, weight loss, and social contact. However, men were more likely to exercise for disease prevention and treatment () than women (OR, 0.72; 95% CI, 0.64-0.80), and women were more likely to exercise for entertainment (OR, 1.29; 95% CI, 1.16-1.45, ) than men. The sociodemographic characteristics of age were significantly associated with each exercise motivation (). Interestingly, participants aged 20-29 years old were most likely to exercise for disease prevention and treatment () far more than participants in other age groups. Of 863 participants aged 20-29 years old, 800 (92.7%) had chosen disease prevention and treatment. Participants aged 30-39 years old (OR, 23.03; 95% CI, 17.48-30.34) were most likely to exercise for entertainment. Middle- to older-aged participants (40-49 years old (OR, 1.66; 95% CI, 1.38-1.99), 50-59 years old (OR, 1.78; 95% CI, 1.48-2.14), and 60-69 years old (OR, 1.40; 95% CI, 1.16-1.86) were more likely to exercise for mood regulation. Compared to younger participants, elderly participants (60-69 years old) preferred to exercise for weight loss (OR, 3.65; 95% CI, 2.79-4.77) and social contact (OR, 3.40; 95% CI, 2.33-4.96).

Our results also showed that participants with an education level of college or junior college or above (OR, 0.8; 95% CI, 0.67-0.95) were less likely to exercise for mood regulation than middle- to high-school or technical secondary school educational level participants. With higher education levels, more participants were likely to exercise for disease prevention and treatment. Students () were most likely to exercise for disease prevention and treatment. Exercise for entertainment seemed more attractive for business service employees (OR, 11.54; 95% CI, 6.27-21.23). Workers (OR, 2.04; 95% CI, 1.29-3.21), administrative staff and professionals (OR, 1.67; 95% CI, 1.08-2.58), homemakers and retired individuals (OR, 1.58; 95% CI, 1.04-2.41), and others (OR, 1.73; 95% CI, 1.12-2.65) were more likely to exercise for mood regulation than students () and business service employees (OR, 1.14; 95% CI, 0.74-1.75). Homemakers and retired participants were most likely to exercise for weight loss (OR, 4.11; 95% CI, 1.90-8.89).

Exercise characteristics included exercise frequency, exercise duration of each session, exercise duration per week, and exercise intensity. In this study, 44.9% (2355/5242) of participants did not engage in physical exercise. Mood regulation had no significant association with exercise frequency, as did increasing physical activity, weight loss, and social contact. Participants whose exercise motivation was disease prevention and treatment were more likely to exercise less than once a week (OR, 1.37; 95% CI, 1.10-1.71), once a week (OR, 1.41; 95% CI, 1.14-1.73), or over four times a week (OR, 1.22; 95% CI, 1.06-1.41) than they were to engage in no exercise () and other exercise frequencies. For the motivation of entertainment, participants were more likely to exercise three times weekly (OR, 1.31; 95% CI, 1.05-1.64) and less likely to exercise four times weekly (OR, 0.79; 95% CI, 0.68-0.91) than they were to engage in no exercise () and other exercise frequencies.

Further, exercise duration for each session or per week showed no significant relationship with mood regulation. For disease prevention and treatment, participants were more likely to exercise less than 30 min (OR, 1.34; 95% CI, 1.07-1.67) and 30-60 min (OR, 1.25; 95% CI, 1.10-1.43) than to engage in no exercise (OR = Ref) and in over 60 min each session (OR, 1.16; 95% CI, 1.00-1.34); they also preferred to exercise 0-30 min (OR, 1.39; 95% CI, 1.14-1.69), 31-60 min (OR, 1.38; 95% CI, 1.11-1.73), and 121-240 min (OR, 1.23; 95% CI, 1.07-1.42) per week than to engage in no exercise () and other duration of exercise per week.

For the motivation of social contact, fewer people preferred to exercise 30-60 min each session (OR, 0.75; 95% CI, 0.58-0.95) than to engage in no exercise () and other duration of exercise each session. People who exercise for entertainment were more likely to exercise 61-120 min per week (OR, 1.28; 95% CI, 1.02-1.63) than to engage in no exercise () and other duration of exercise per week. Participants who exercise to increase physical activity were less likely to exercise 0-30 min per week (OR, 0.72; 95% CI, 0.54-0.97) than to engage in no exercise () and other duration of exercise per week.

Our findings also showed no significant relationship between exercise intensity, mood regulation, and other exercise motivations, such as entertainment and weight loss. Exercise intensity only showed a significant association between disease prevention and treatment () and social contact (). Participants who exercised for the motivation of disease prevention and treatment were more likely to engage in moderate- (OR, 1.22; 95% CI, 1.07-1.39) or heavy-intensity exercise (OR, 1.48; 95% CI, 1.25-1.75). Participants who exercise for social contact were more likely to engage in mild-intensity exercise (OR, 1.35; 95% CI, 1.03-1.78).

Exercise motivations showed no significant association with abdominal obesity () and peptic ulcer (), which is why these factors were not listed in Table 2. BMI only had a significant association with disease prevention and treatment, and the underweight participants () were most likely to choose this motivation. Our research found no significant association between mood regulation and all kinds of diseases. Fewer participants in our study preferred to exercise for disease prevention and treatment with respect to hypertension (OR, 0.65; 95% CI, 0.55-0.77), hyperlipidemia (OR, 0.76; 95% CI, 0.60-0.96), hypercholesterolemia (OR, 0.73; 95% CI, 0.57-0.93), osteoarthropathy (OR, 0.71; 95% CI, 0.57-0.89), and cervical spondylopathy (OR, 0.76; 95% CI, 0.64-0.91). Participants with hypertension preferred to exercise to increase physical activity (OR, 1.35; 95% CI, 1.10-0.65) and lose weight (OR, 1.60; 95% CI, 1.30-1.95).

Concerning indexes 1 and 2, only 16.64% (872/5242) were compliant with index 1, and 2.84% (149/5242) were compliant with index 2. Participants with higher education levels were more likely to meet both indexes 1 and 2 (Table 3). More participants with education levels of college/junior college or above (OR, 3.41; 95% CI, 1.74-6.66) and high/technical secondary school (OR, 2.13; 95% CI, 1.10-4.10) met index 1 than did participants with education levels of middle school (OR, 1.49; 95% CI, 0.77-2.87) and primary or below (). More participants with education levels of college/junior college or above (OR, 2.08; 95% CI, 1.58-2.75) and high/technical secondary school (OR, 1.76; 95% CI, 1.36-2.28) met index 2 than did participants with education levels of middle school (OR, 1.30; 95% CI, 1.00-1.67) and primary school () (Table 4).

4. Discussion

In terms of gender differences in depression, major depression is generally believed to be twice as prevalent among women than in men and represents a major health disparity [33]. In this study, rather than a 2-fold difference, no gender difference was observed among those who exercised for mood regulation. Similar to mood regulation, there was also no gender difference for increasing physical activity, weight loss, and social contact. However, men were more likely than women to exercise for disease prevention and treatment, and women were more likely than men to exercise for entertainment. Female sex was a significant sociodemographic correlate of low physical activity [34, 35]. The rate of no exercise among women was 43.0% (1066/2480), which was lower than the corresponding rate among men (46.7%, 1289/2762, value = 0.007). Generally, women have hobbies that can keep them at home longer than men, while men tend to engage in more outdoor physical recreational activities [36]. Physical activity for leisure was not associated with depression among Brazilian females, and this association was significant among Brazilian males, who might be able to benefit from physical activity for leisure to reduce their symptoms of depression [37].

The reasons why people engage in physical activity might differ among young, middle, and older adults as a result of changing ideologies, life tasks, goals, and health circumstances overtime [38]. The exact manner in which motivation to exercise shifts with increasing age was not fully understood. The motivators of people exercising were proven to shift from future-oriented goals to more present-focused and emotionally meaningful goals as they aged. It was revealed that younger adults prefer to convey expert knowledge of instrumental support, helping to facilitate and engage in challenging workouts, but distinct age-related prioritizations of social factors emerged in motivations to exercise, emphasizing the maintenance and fostering of relationships and increased opportunities for socializing [39]. In our study, young participants in 20-29 years old were most likely to exercise to prevent and treat disease, which was a future-oriented goal. Those between 30 and 39 years of age were most likely to exercise for entertainment as a transition stage, as they had more financial stability than younger participants and fewer worries than older participants related to work stress, depression, illness, obesity, and social requests. Older participants between 40 and 69 years were more likely than younger participants to exercise for mood regulation as both an emotional and meaningful present-focused goal. Those between 60 and 69 years of age were more likely to exercise for social contact as an emotionally meaningful goals and for weight loss as a present-focused goal. We recommend that exercises promoting both physical and mental health should be more popularized among younger adults.

Our study also showed that participants with an average level of education (high school/technical secondary school) were more likely to exercise for mood regulation than those with a lower or higher level of education. Similarly, in a behavior change intervention study, it was more difficult for groups with low and high levels of education and participants with mental diseases to adhere to exercise interventions than for others [40].

Jobs and mood disorders have generally been found to be correlated with each other [41–43]. Studies have shown that engaging in exercise served as a method of mood regulation to help people experience a positive mood for lengthier periods of time [44]. In our study, workers, administrative staff and professionals, and homemakers and retired individuals were more likely to exercise for mood regulation than students and business service employees. However, the job classification method of the questionnaire we used could not classify jobs involving engagement in high levels of physical activity, which reminded us that the results of our study should be interpreted with caution.

Physical inactivity and depression are involved in cause-and-effect relationships. Adults aged 18–64 years should perform at least 150 minutes of moderate-intensity aerobic physical activity throughout the week, at least 75 minutes of heavy-intensity aerobic physical activity throughout the week, or an equivalent combination of moderate- and heavy-intensity activity [45]. One-fourth of the European Union population did not meet the WHO recommendations for physical activity [46]. Depressed people were typically less active, while lower levels of physical activity increased the risk of depression [47]. It is well-known that engagement in regular physical activity is an important part of a healthy lifestyle. However, it was estimated that 60% of the United States population does not exercise regularly [48], and 63% of Canadians were not sufficiently active to obtain these health benefits [49]. Similarly, 44.9% (2355/5242) of participants in our study did not engage in physical exercise.

Exercise is a purpose-directed activity, and numerous investigations have studied individuals’ reasons to exercise, such as health risks [50], treatment for depression [17], and social contact [38]. However, not all the exercise motivations were significantly related to physical activity because continuously maintaining a motivational attitude for a long period of time requires determination and can be quite challenging for many people, especially during this current busy daily life [51]. It was first expected that different exercise motivations would predict different physical practice frequencies, as various exercise motivations have been found to influence the effort expended during exercise [52]. Significant relationships were observed between mental health and both exercise frequency and duration [16]. The fact was mood regulation had no significant association with exercise frequency in our study, as did increasing physical activity, weight loss, and social contact. For the exercise motivation of disease prevention and treatment, participants were more likely to exercise less than once a week, once a week, or over four times a week than they were to exercise at other frequencies. With respect to entertainment, participants were more likely to exercise three times weekly and less likely to exercise over four times weekly than they were to engage in no exercise or exercise at other frequencies.

For exercise duration, a longer period of time is not necessarily better. Chekroud’s study indicated that exercise duration between 30 min and 60 min (peaking at approximately 45 min) was associated with the lowest occurrence of mental health problems. A duration of more than 3 hours was associated with worse mental health [16]. However, in our study, exercise duration for each session or per week both showed no significant relationship with mood regulation. For the exercise motivation of disease prevention and treatment, participants were more likely to do exercise for less than 30 min or 30-60 min each session than they were to engage in no exercise or over 60 min of exercise each session; additionally, they prefer to exercise 0-30 min, 31-60 min, and 121-240 min per week than to engage in no exercise or other duration per week. For the motivation of social contact, fewer people would like to exercise for 30-60 min each session than to engage in no exercise or other duration of exercise each session. People who exercised for entertainment were more likely to exercise 61-120 min per week than to engage in no exercise or other duration per week. It was not surprising that participants who exercised to increase physical activity were less likely to exercise 0-30 min per week than they were to engage in no exercise or other duration per week.

Exercise motivations were found to have a strong relationship with exercise intensity [49]. Exercise of any intensity significantly regulated mood with no differential effect following mild-intensity, moderate-intensity, or heavy-intensity exercise [53]. Similarly, our research also showed no significant relationship between exercise intensity and mood regulation. A significant association was found between exercising for disease prevention and treatment and moderate and heavy exercise intensity. Some research has shown that heavy-intensity exercise can lead to health benefits above and beyond those offered by moderate-intensity exercise [54]. It was not surprising that participants exercising for the motivation of social contact were more likely to engage in mild-intensity exercise, sparing more energy for social activity. However, mild-intensity exercise showed no significant association with other exercise motivations, such as entertainment or weight loss.

Studies have shown that regular exercise is linked to the prevention of many chronic diseases, such as depression, type 2 diabetes, hypertension, obesity, and osteoporosis [55, 56]. However, changing exercise behaviors and maintaining the level of exercise required to reduce disease risk could be difficult to achieve, even when one is engaged in a lifestyle-change program [57]. Emotional problems played a key role in inactivity among individuals with chronic diseases [58, 59], but they showed no significant association between mood regulation and all kinds of diseases in our research. People with chronic diseases were more likely to be physically inactive, regardless of whether they had weak or strong intentions to exercise [60]. This might be the reason why fewer participants in our study would like to exercise for disease prevention and treatment with respect to hypertension, hyperlipidemia, hypercholesterolemia, osteoarthropathy, and cervical spondylopathy. Instead, participants preferred to exercise for increasing physical activity and weight loss. In a study of exercise motivation and weight loss among patients with hypertension, the majority reported being in the action stage or higher for weight loss [27].

For indexes 1 and 2, the compliance rates were very low. Only 16.64% (872/5242) were compliant with index 1, and 2.84% (149/5242) were compliant with index 2. One possible reason was that many patients with depression were physically inactive [61]. A study in Finland comprised 645 people and demonstrated that the risk of physical inactivity was more than twofold among persons with depressive symptoms than among nondepressed people [62]. In the Upper Bavarian Field Study, a representative community sample of 1,536 persons, people with depression were approximately three times more likely to be physically inactive than those who exercised regularly [63]. Another important result of our study was that higher education showed a significant association with reducing the mental burden and preventing depression. Participants who had a lower educational level were less likely to meet both indexes 1 and 2. This finding might be that the less-educated individuals have limited health consciousness and less knowledge about the advantages of physical activity than do more highly educated individuals [64]. The intervention study aiming to improve the level of education appeared successful in improving physical activity [65].

The limitations of our study should be considered. Firstly, our study was a cross-sectional design, and causal inferences cannot be made. Prospective study designs should be considered in further research. Secondly, it should be noted that this study did not investigate the status of depression. While this study did provide some insight into the link between exercise motivation and various sociodemographic characteristics, exercise behaviors, and physical conditions, the results must be interpreted with some degree of caution, as all of the exercise behavior measurements were provided via self-report. Thirdly, exercise measures were provided via self-reported in this study, and participants may overreport their frequencies or duration of exercise.

5. Conclusion

This study included six specific exercise motivations to identify the most important motivational forces behind sociodemographic characteristics and physical conditions and to understand motivation decisions leading to engagement in exercise of varying frequency, intensity, and durations. We also compared the differences between mood regulation and other exercise motivations and the possible influencing factors of exercise status in conjunction with mood regulation. Overall, the present investigation provides an important first step in determining the motivation profile of adults and older adults who exercise, which can be used as a reference to develop proper interventions in the future.

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Disclosure

A preprint has previously been published [66] [https://www.researchsquare.com/article/rs-15572/v1].

Conflicts of Interest

The authors declare that they have no competing interests.

Authors’ Contributions

Jingjing Lou and Nianhong Guan contributed equally to this work as cocorresponding author. Jingjing Lou and Tong Li contributed equally to this work as co-first author.

Acknowledgments

This study received funding from the Psychiatry Course Teaching Team of Guangdong Province. The authors would like to thank and acknowledge the dedicated work of the Guangzhou Institute of Sports Science who conducted the Guangdong National Physique Monitoring Survey and the survey participants who made this study possible.

References

World Health Organization, “Depression and other common mental disorders,” 2017, https://www.who.int/mental_health/management/depression/prevalence_global_health_estimates/en/.
View at: Google Scholar
M. Bauer, E. Severus, H.-J. Möller, A. Young, and WFSBP Task Force on Unipolar Depressive Disorders, “Pharmacological treatment of unipolar depressive disorders: summary of WFSBP guidelines,” vol. 21, no. 3, pp. 166–176, 2017.
View at: Publisher Site | Google Scholar
D. Bennabi, A. Yrondi, T. Charpeaud et al., “Clinical guidelines for the management of depression with specific comorbid psychiatric conditions French recommendations from experts (the French Association for Biological Psychiatry and Neuropsychopharmacology and the fondation FondaMental),” BMC Psychiatry, vol. 19, no. 1, p. 50, 2019.
View at: Publisher Site | Google Scholar
C. J. Murray, T. Vos, R. Lozano et al., “Disability-adjusted life years (DALYs) for 291 diseases and injuries in 21 regions, 1990-2010: a systematic analysis for the global burden of disease study 2010,” Lancet, vol. 380, no. 9859, pp. 2197–2223, 2012.
View at: Publisher Site | Google Scholar
D. Chisholm, K. Sweeny, P. Sheehan et al., “Scaling-up treatment of depression and anxiety: a global return on investment analysis,” Lancet Psychiatry, vol. 3, no. 5, pp. 415–424, 2016.
View at: Publisher Site | Google Scholar
D. G. Blazer, “Depression in late life: review and commentary,” The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences, vol. 58, no. 3, pp. 249–265, 2003.
View at: Google Scholar
K. Smith, “Mental health: a world of depression,” Nature, vol. 515, no. 7526, pp. 180-181, 2014.
View at: Publisher Site | Google Scholar
F. B. Schuch, D. Vancampfort, J. Richards, S. Rosenbaum, P. B. Ward, and B. Stubbs, “Exercise as a treatment for depression: a meta-analysis adjusting for publication bias,” Journal of Psychiatric Research, vol. 77, pp. 42–51, 2016.
View at: Publisher Site | Google Scholar
M. Toups, T. Carmody, T. Greer, C. Rethorst, B. Grannemann, and M. H. Trivedi, “Exercise is an effective treatment for positive valence symptoms in major depression,” Journal of Affective Disorders, vol. 209, pp. 188–194, 2017.
View at: Publisher Site | Google Scholar
H. Mothes, S. Klaperski, H. Seelig, S. Schmidt, and R. Fuchs, “Regular aerobic exercise increases dispositional mindfulness in men: a randomized controlled trial,” Mental Health and Physical Activity, vol. 7, no. 2, pp. 111–119, 2014.
View at: Publisher Site | Google Scholar
A. A. Nierenberg and L. M. DeCecco, “Definitions of antidepressant treatment response, remission, nonresponse, partial response, and other relevant outcomes: a focus on treatment-resistant depression,” Journal of Clinical Psychiatry, vol. 62 Suppl 16, pp. 5–9, 2001.
View at: Google Scholar
A. Wright and M. Cattan, “Physical activity and the management of depression,” Working with Older People, vol. 13, no. 1, pp. 15–18, 2009.
View at: Publisher Site | Google Scholar
J. A. Blumenthal, A. Sherwood, M. A. Babyak et al., “Exercise and pharmacological treatment of depressive symptoms in patients with coronary heart disease: results from the UPBEAT (understanding the prognostic benefits of exercise and antidepressant therapy) study,” Journal of the American College of Cardiology, vol. 60, no. 12, pp. 1053–1063, 2012.
View at: Publisher Site | Google Scholar
G. M. Cooney, K. Dwan, C. A. Greig et al., “Exercise for depression,” Cochrane Database of Systematic Reviews, vol. 9, 2000.
View at: Publisher Site | Google Scholar
S. Gujral, H. Aizenstein, C. F. Reynolds, M. A. Butters, and K. I. Erickson, “Exercise effects on depression: possible neural mechanisms,” General Hospital Psychiatry, vol. 49, pp. 2–10, 2017.
View at: Publisher Site | Google Scholar
S. R. Chekroud, R. Gueorguieva, A. B. Zheutlin et al., “Association between physical exercise and mental health in 1*2 million individuals in the USA between 2011 and 2015: a cross-sectional study,” Lancet Psychiatry, vol. 5, no. 9, pp. 739–746, 2018.
View at: Publisher Site | Google Scholar
S. B. Harvey, S. Overland, S. L. Hatch, S. Wessely, A. Mykletun, and M. Hotopf, “Exercise and the prevention of depression: results of the HUNT cohort study,” The American Journal of Psychiatry, vol. 175, no. 1, pp. 28–36, 2018.
View at: Publisher Site | Google Scholar
H. Y. Lee, C. P. Yu, C. D. Wu, and W. C. Pan, “The effect of leisure activity diversity and exercise time on the prevention of depression in the middle-aged and elderly residents of Taiwan,” International journal of environmental research and public health, vol. 15, no. 4, p. 654, 2018.
View at: Publisher Site | Google Scholar
J. Reed and S. Buck, “The effect of regular aerobic exercise on positive-activated affect: a meta-analysis,” Psychology of Sport and Exercise, vol. 10, no. 6, pp. 581–594, 2009.
View at: Publisher Site | Google Scholar
World Health Organization, Global Recommendations on Physical Activity for Health, World Health Organization, 2010, Copyright (c) World Health Organization 2010.).
P. C. Hallal, L. B. Andersen, F. C. Bull et al., “Global physical activity levels: surveillance progress, pitfalls, and prospects,” Lancet, vol. 380, no. 9838, pp. 247–257, 2012.
View at: Publisher Site | Google Scholar
N. Howlett, A. Jones, L. Bain, and A. Chater, “How effective is community physical activity promotion in areas of deprivation for inactive adults with cardiovascular disease risk and/or mental health concerns? Study protocol for a pragmatic observational evaluation of the’Active Herts' physical activity programme,” BMJ Open, vol. 7, article e017783, 2017.
View at: Google Scholar
G. Bishwajit, D. P. O’Leary, S. Ghosh, S. Yaya, T. Shangfeng, and Z. Feng, “Physical inactivity and self-reported depression among middle- and older-aged population in South Asia: world health survey,” BMC Geriatrics, vol. 17, no. 1, p. 100, 2017.
View at: Publisher Site | Google Scholar
S. Win, K. Parakh, C. M. Eze-Nliam, J. S. Gottdiener, W. J. Kop, and R. C. Ziegelstein, “Depressive symptoms, physical inactivity and risk of cardiovascular mortality in older adults: the cardiovascular health study,” Heart, vol. 97, no. 6, pp. 500–505, 2011.
View at: Publisher Site | Google Scholar
D. Vancampfort, B. Stubbs, J. Firth et al., “Physical activity correlates among 24,230 people with depression across 46 low- and middle-income countries,” Journal of Affective Disorders, vol. 221, pp. 81–88, 2017.
View at: Publisher Site | Google Scholar
A. E. Bauman, R. S. Reis, J. F. Sallis et al., “Correlates of physical activity: why are some people physically active and others not?” The Lancet, vol. 380, no. 9838, pp. 258–271, 2012.
View at: Publisher Site | Google Scholar
J. Warren, K. Bryant Smalley, and K. Barefoot, “Discrepancy in motivation for weight loss and exercise in rural patients,” American journal of health behavior, vol. 41, no. 6, pp. 803–809, 2017.
View at: Publisher Site | Google Scholar
J. Fisher, A. Sales, L. Carlson, and J. Steele, “A comparison of the motivational factors between CrossFit participants and other resistance exercise modalities: a pilot study,” The Journal of Sports Medicine and Physical Fitness, vol. 57, no. 9, pp. 1227–1234, 2017.
View at: Publisher Site | Google Scholar
B. Berger and R. W. Motl, “Exercise and mood: a selective review and synthesis of research employing the profile of Mood states,” Journal of applied sport psychology, vol. 12, no. 1, pp. 69–92, 2000.
View at: Publisher Site | Google Scholar
Y. Tian, C. Jiang, M. Wang et al., “BMI, leisure-time physical activity, and physical fitness in adults in China: results from a series of national surveys, 2000–14,” The Lancet Diabetes & Endocrinology, vol. 4, no. 6, pp. 487–497, 2016.
View at: Publisher Site | Google Scholar
“Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies,” Lancet, vol. 363, no. 9403, pp. 157–163, 2004.
View at: Publisher Site | Google Scholar
P. Z. Zimmet and K. G. Alberti, “Introduction: globalization and the non-communicable disease epidemic,” Obesity (Silver Spring), vol. 14, no. 1, pp. 1–3, 2006.
View at: Publisher Site | Google Scholar
R. H. Salk, J. S. Hyde, and L. Y. Abramson, “Gender differences in depression in representative national samples: meta-analyses of diagnoses and symptoms,” Psychological Bulletin, vol. 143, no. 8, pp. 783–822, 2017.
View at: Publisher Site | Google Scholar
A. Koyanagi, B. Stubbs, and D. Vancampfort, “Correlates of low physical activity across 46 low- and middle-income countries: a cross-sectional analysis of community-based data,” Preventive Medicine, vol. 106, pp. 107–113, 2018.
View at: Publisher Site | Google Scholar
C. J. Caspersen, M. A. Pereira, and K. M. Curran, “Changes in physical activity patterns in the United States, by sex and cross-sectional age,” Medicine and Science in Sports and Exercise, vol. 32, no. 9, pp. 1601–1609, 2000.
View at: Google Scholar
C. Walter and R. du Randt, “Socio-cultural barriers to physical activity among black isiXhosa speaking professional women in the Nelson Mandela Metropolitan Municipality,” South African Journal for Research in Sport, Physical Education and Recreation, vol. 33, no. 2, 2011.
View at: Publisher Site | Google Scholar
G. D. de Oliveira, S. C. Oancea, L. B. Nucci, and N. Vogeltanz-Holm, “The association between physical activity and depression among individuals residing in Brazil,” Social Psychiatry and Psychiatric Epidemiology, vol. 53, no. 4, pp. 373–383, 2018.
View at: Publisher Site | Google Scholar
J. Brunet and C. Sabiston, “Exploring motivation for physical activity across the adult lifespan,” Psychology of sport and exercise, vol. 12, no. 2, pp. 99–105, 2011.
View at: Publisher Site | Google Scholar
C. N. Steltenpohl, M. Shuster, E. Peist, A. Pham, and J. Mikels, Me Time, or We Time? Age Differences in Motivation for Exercise, 2018.
G. Samdal, E. Meland, G. Eide et al., The Norwegian Healthy Life Centre Study: A Pragmatic RCT of Physical Activity in Primary Care, The Gerontologist, vol. 59, no. 4, 2019.
S. Stansfeld and B. Candy, “Psychosocial work environment and mental health—a meta-analytic review,” Scandinavian Journal of Work, Environment & Health, vol. 32, no. 6, pp. 443–462, 2006.
View at: Publisher Site | Google Scholar
S. A. Stansfeld, R. Fuhrer, M. J. Shipley, and M. G. Marmot, “Work characteristics predict psychiatric disorder: prospective results from the Whitehall II study,” Occupational and Environmental Medicine, vol. 56, no. 5, pp. 302–307, 1999.
View at: Publisher Site | Google Scholar
S. Paterniti, I. Niedhammer, T. Lang, and S. Consoli, “Psychosocial factors at work, personality traits and depressive symptoms,” The British Journal of Psychiatry, vol. 181, no. 2, pp. 111–117, 2002.
View at: Publisher Site | Google Scholar
S. Toker and M. Biron, “Job burnout and depression: unraveling their temporal relationship and considering the role of physical activity,” Journal of Applied Psychology, vol. 97, no. 3, p. 699, 2012.
View at: Google Scholar
World Health Organization, “Global recommendations on physical activity for health 18-64 years old,” 2011.
View at: Google Scholar
V. Gerovasili, I. T. Agaku, C. I. Vardavas, and F. T. Filippidis, “Levels of physical activity among adults 18-64 years old in 28 European countries,” Preventive Medicine, vol. 81, pp. 87–91, 2015.
View at: Publisher Site | Google Scholar
F. Schuch, D. Vancampfort, J. Firth et al., “Physical activity and sedentary behavior in people with major depressive disorder: a systematic review and meta-analysis,” Journal of Affective Disorders, vol. 210, pp. 139–150, 2017.
View at: Publisher Site | Google Scholar
W. D. Clark U.S., “Exercise and the brain,” Brain Imaging in Behavioral Medicine and Clinical Neuroscience, Springer, New York, NY, 2011.
View at: Google Scholar
L. R. Duncan, C. R. Hall, P. M. Wilson, and O. Jenny, “Exercise motivation: a cross-sectional analysis examining its relationships with frequency, intensity, and duration of exercise,” International Journal of Behavioral Nutrition and Physical Activity, vol. 7, no. 1, pp. 7–9, 2010.
View at: Publisher Site | Google Scholar
D. B. Portnoy, A. R. Kaufman, W. M. P. Klein, T. A. Doyle, and M. de Groot, “Cognitive and affective perceptions of vulnerability as predictors of exercise intentions among people with type 2 diabetes,” Journal of Social and Clinical Psychology, vol. 17, no. 2, pp. 177–193, 2014.
View at: Publisher Site | Google Scholar
D. B. Feldman, K. Rand, and K. Kahle-Wrobleski, “Hope and goal attainment: testing a basic prediction of hope theory,” Journal of Social and Clinical Psychology, vol. 28, no. 4, pp. 479–497, 2009.
View at: Publisher Site | Google Scholar
P. Wilson, W. Rodgers, S. Fraser, and T. Murray, “Relationships between exercise regulations and motivational consequences in university students,” Research quarterly for exercise and sport, vol. 75, no. 1, pp. 81–91, 2004.
View at: Publisher Site | Google Scholar
J. D. Meyer, K. F. Koltyn, A. J. Stegner, J. S. Kim, and D. B. Cook, “Influence of exercise intensity for improving depressed mood in depression: a dose-response study,” Behavior Therapy, vol. 47, no. 4, pp. 527–537, 2016.
View at: Publisher Site | Google Scholar
P. T. Williams and B. Franklin, “Vigorous exercise and diabetic, hypertensive, and hypercholesterolemia medication use,” Medicine and Science in Sports and Exercise, vol. 39, no. 11, pp. 1933–1941, 2007.
View at: Publisher Site | Google Scholar
D. E. R. Warburton, C. W. Nicol, and S. S. D. Bredin, “Health benefits of physical activity: the evidence,” CMAJ: Canadian Medical Association journal = journal de l’Association medicale canadienne, vol. 174, no. 6, pp. 801–809, 2006.
View at: Publisher Site | Google Scholar
S. Black, J. Goodwin, and K. Markides, “The association between chronic diseases and depressive symptomatology in older Mexican Americans,” The Journals of Gerontology Series A: Biological Sciences and Medical Sciences, vol. 53, no. 3, pp. M188–M194, 1998.
View at: Publisher Site | Google Scholar
M. Alharbi, R. Gallagher, L. Neubeck et al., “Exercise barriers and the relationship to self-efficacy for exercise over 12 months of a lifestyle-change program for people with heart disease and/or diabetes,” European Journal of Cardiovascular Nursing, vol. 16, no. 4, pp. 309–317, 2017.
View at: Publisher Site | Google Scholar
M. Alharbi, R. Gallagher, A. Kirkness, D. Sibbritt, and G. Tofler, “Long-term outcomes from Healthy Eating and Exercise Lifestyle Program for overweight people with heart disease and diabetes,” European Journal of Cardiovascular Nursing, vol. 15, no. 1, pp. 91–99, 2016.
View at: Publisher Site | Google Scholar
L. M. Campkin, J. Boyd, and D. Campbell, “Coronary artery disease patient perspectives on exercise participation,” Journal of Cardiopulmonary Rehabilitation and Prevention, vol. 37, no. 5, pp. 305–314, 2017.
View at: Publisher Site | Google Scholar
B. Sassen, G. Kok, H. Schaalma, H. Kiers, and L. Vanhees, “Cardiovascular risk profile: cross-sectional analysis of motivational determinants, physical fitness and physical activity,” BMC Public Health, vol. 10, no. 1, p. 592, 2010.
View at: Publisher Site | Google Scholar
M. A. Whooley, P. de Jonge, E. Vittinghoff et al., “Depressive symptoms, health behaviors, and risk of cardiovascular events in patients with coronary heart disease,” JAMA, vol. 300, no. 20, pp. 2379–2388, 2008.
View at: Publisher Site | Google Scholar
E. Rosqvist, E. Heikkinen, T. M. Lyyra et al., “Factors affecting the increased risk of physical inactivity among older people with depressive symptoms,” Scandinavian Journal of Medicine & Science in Sports, vol. 19, no. 3, pp. 398–405, 2009.
View at: Publisher Site | Google Scholar
S. Weyerer, “Physical inactivity and depression in the community,” International Journal of Sports Medicine, vol. 13, no. 6, pp. 492–496, 1992.
View at: Publisher Site | Google Scholar
I. Mesters, S. Wahl, and H. M. Van Keulen, “Socio-demographic, medical and social-cognitive correlates of physical activity behavior among older adults (45-70 years): a cross-sectional study,” BMC Public Health, vol. 14, no. 1, pp. 647–647, 2014.
View at: Publisher Site | Google Scholar
H. M. van Keulen, I. Mesters, M. Ausems et al., “Tailored print communication and telephone motivational interviewing are equally successful in improving multiple lifestyle behaviors in a randomized controlled trial,” Annals of behavioral medicine: a publication of the Society of Behavioral Medicine, vol. 41, no. 1, pp. 104–118, 2011.
View at: Publisher Site | Google Scholar
T. Li, J. Lou, Y. Hu et al., Who Exercises for Mood Regulation? Results from the Guangdong National Physique Monitoring Data, Research Square, 2020.
View at: Publisher Site

Copyright

Copyright © 2022 Jingjing Lou et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

PDF Download Citation

Download other formats

Order printed copies

Views

432

Downloads

351

Citations