Association between health behaviors and mood disorders among the elderly: a community-based cohort study

The ageing population is growing rapidly. The World Health Organization (WHO) estimated that the proportion of the elderly population in the world will jump from 12 to 22% between 2015 and 2050. Over 20% of elderly persons have suffered from mental or neurological disorders [1]. In addition, several community-based cohort studies have found that mood disorders are an important risk factor for suicide in the elderly, and significantly reduce the quality of life and increase disease burden [2]. According to a National Health Service (NHS) report in the UK, half of adults (7.7million) aged 55 and over have experienced common mental health problems [3]. Therefore, the UK government established a plan to help the elderly through general practitioners (GP) and a complete community care system to detect the signs of mood disorders early [4, 5]. In the United States, 18.29% of adults (nearly 43.7 million Americans) struggle with mental health problems annually [6]. Additionally, the proportion of older adults who experience mood disorders is 6.8% [7]. A study with a sample of 3142 older men and women (aged 65–84 years) indicated that one in two individuals had experienced mental disorders in their lifetime, one in three within the past year, and nearly one in four currently had a mental disorder [8]. In Asia, including the elderly, about 8.8% of people aged above 20 suffer mood disorders in Japan [9]. With a growing proportion of older adults in Japan, the number of older adult patients (≥65 years old) with mood or anxiety disorders has also been increasing, reaching 340,000 in 2014 [10]. In the context of this rapid population aging, there is a growing interest in promoting healthy aging, including the mental health of the elderly.

There are multiple risk factors for mental health problems in the elderly, such as demographic characteristics, e.g., being female, unmarried, living alone, or children not being around [11]. Socio-economic indicators such as low household income [11], low education level [12], and other individual characteristics like chronic diseases, lack of participation in community sports or physical activities [13], physical illness, and poor health status are all associated with higher rates of mood disorders [14]. In addition, one study found that regular physical activity in midlife is significantly associated with reduced depressive symptoms 25 years later [15]. Previous research also indicated that physical activity is a protective factor for mental health in older adults [16]. However, light physical activity, independent of moderate to vigorous physical activity, is associated with fewer depressive symptoms [17]. In summary, the past studies pointed out that being female [11], divorced [11], living alone [11], low education level [12], not doing physical activity [13, 15], having chronic diseases, and bad health status are correlated with mood disorder in the elderly [14]. However, the correlation between smoking behavior and mood disorder in the elderly has diverse conclusions in the literature [18, 19]. Some health behaviors considered in this study such as drinking alcohol, eating vegetables and fruits, and drinking milk are not mentioned in past studies. In addition, this study collected a wide spectrum of the elderly population, including healthy and sick elderly in the communities, and provided the opportunity to identify the changeable behaviors in daily life.

Preventive intervention can reduce the disease burden of mood disorders. Previous studies have indicated that prevention and intervention in the early stages are very effective to reduce severity and prevent secondary disorders, such as degradation of cognitive functions [20]. Sarris and colleagues [21] provide a nexus between public health promotion and clinical treatments, involving the application of environmental and behavioral interventions to enhance physical and mental well-being, calling it “Life Medicine.” However, these studies recruited a wide age range of subjects, or focused on the disease of depression itself. As far as we know, there is little information on how to use community health examinations to identify mood disorders or emotional problems in the elderly [18]. In addition, few studies have large samples and discuss health in the elderly in a community with a longitudinal cohort.

In this study, we applied the Taipei City Elderly Health Examination Database for a total of eight years of follow-up data to identify possible risk factors early. The database included not only the basic physical characteristics of the individual, but also variables of healthy behavior, for example, the frequency of smoking, drinking alcohol, frequency of physical activity, and the number of underlying diseases and drug history. The primary objective of this study was to elucidate the potential risk factors associated with mood disorders through using BSRS-5 (5-item Brief Symptom Rating Scale) scores [22]. The second aim of this study was to find risk and protective factors with mood disorders, so as to be able to provide health policy makers suggestions for health education intervention to reduce the risk of mood disorders in the elderly.

This study applied long-term follow-up data on the elderly participating in the Taipei health examination at least twice to analyze the possible risk factors for mood disorders in the elderly. In our study, we collected all potential risk factors from the health examination. In addition to finding out the risk factors from the existing literature, we also tried to examine some new factors from our unique database. Although elderly mental health is an important issue in past studies, this study demonstrated different kinds of potential risk factors together and had multiple repeated measurements to validate the results. We found not only risk factors, but also protective factors for mood disorders in the elderly simultaneously in one study. The results suggest that health policy makers implement behavior intervention to reduce mood disorders with health education. We found that the residents living in districts with a higher average educational level were more willing to participate in the health examination. In addition, we also identified the personal demographic factors, health behaviors and self-report of medication factors associated with mood disorders.

Our results reveal that the demographic variables of female, older age, lower education level, and divorced status are associated with mood disorders. Specifically, previous studies have shown that gender [28, 29] and age [30] are associated with depression. The results in the present study are consistent with domestic and international research, which indicated that gender difference also plays a role in mental health [31, 32]. For the elderly, our study illustrates that marital status (particularly, divorce) is a key factor associated with mood disorders. One study in Singapore found that females have higher depressive symptom scores than males. However, living alone and weak social networks outside the household were associated with higher depressive symptoms in both males and females [29]. Hence, for the elderly, living alone, not being married [18], and weak social networks may all increase their emotional isolation and social disengagement. They are consequently more likely to have mood disorders. Higher education seems to be a protective factor in the elderly [33]. A previous study showed that higher social activity was associated with a lower risk of late-life depressive symptoms at a baseline in a large community sample with 2 years of follow-up [34]. Another study, in Korea, founded that those who participated in frequent physical, social, and some religious activity had a low odds ratio for depression compared with their counterparts [35]. In summary, these elderly with few social network links and/or who are divorced may have high risk of mood disorders because they are lonely and seldom interact with others.

The percentage of elderly people who participated in the health examination in this study who were living in Datong, Nangang and Neihu districts is low. The proportion with a high school education and above in these areas is low as well (less than 50%). The above results lead us to speculate that education level may affect health risk awareness. People with higher education level are more concerned about their health and willing to conduct regular health examinations [36‐38]. In addition, we found that those with lower education levels were more likely to suffer from mood disorders, which was consistent with results of previous studies [30, 39, 40]. Low economic status may be associated with educational opportunity and less chance to access medical service [41]. The worst geographical areas in terms of mood disorders are Wenshan, Shilin and Zhongshan districts. In contrast, the areas with the highest proportion of normal mood status are Nangang, Xinyi, Da’an and Zhongzheng districts. Among them, people in Xinyi and Da’an districts had senior high school education level in a higher proportion than other districts. These areas are important business and economic areas. Therefore, the better mood may be owing to having a better material life for the elderly [42, 43]. In contrast, low education level and economic status may elevate life pressure, which may directly or indirectly worsen mood status.

Additionally, lifestyle is the most important factor affecting health [44]. Healthy behaviors, for example, food and alcohol intake, smoking and frequency of physical activity, are included. Our study also revealed the lifestyle characteristics of the elderly that may be associated with mood disorders. We found that drinking milk, as well as eating at least three servings of vegetables and two servings of fruits, is associated with a lower chance of developing mood disorders in the elderly. The present study found that elderly people who had no depressive symptoms were more likely to consume well-balanced meals and milk products on a daily basis [45]. In addition, one study illustrated that adherence to a Mediterranean dietary pattern ensures an adequate intake of fruits, nuts, vegetables, cereals, legumes and fish, all of which are important sources of nutrients linked to lower risk of depression [46]. Another study indicated that intake of milk products is also thought to be related to reduced depressive symptoms among elderly people [47]. Calcium and milk intake is indirectly related to depressive symptoms because it prevents central adiposity, which has been associated with depression [48]. This finding supports current recommendations for increasing milk, fruit and vegetable intake to improve mental health. Moreover, Nakao and Yano (2004) determined that depression is associated with high serum cholesterol levels among middle-aged Japanese men [19]. This is similar to our finding that higher cholesterol levels were associated with mood disorders in the elderly.

On the other hand, unhealthy behaviors, for example, daily smoking, and never exercising, were risk factors of mood disorders. Two previous studies have illustrated that people who smoke or have smoked are more depressed than those who have not done so before, but these findings were significant in only one of the two studies [18, 19]. However, occasionally drinking alcohol has a mild protective effect against mood disorders. This result is also similar to a previous study, which indicated that moderate alcohol consumption in women was related to fewer depressive symptoms in later life [48]. Additionally, previous studies have revealed that leisure activity participation can improve psychological well-being in older adults [49]. Similar to our findings, those suggest that participation in recreational physical activity is associated with a lower chance of developing mood disorders among the elderly. The mental happiness achieved through physical activity enables the elderly to reduce their stress.

Furthermore, poor health status has been demonstrated to be a risk factor for depression in the elderly, such as having a history of stroke, cancer or Parkinson’s disease [50, 51]. Patients may feel frustrated because they lose their self-care ability and feel they are troublesome to their families. Among our participants, their self-reported history of medication and chronic disease showed that having more chronic diseases is associated with mood disorders among the elderly.

Previous studies have indicated that there are many risk factors related to depression. From the perspective of preventive medicine, mental health of the elderly in the community is very important. Through community monitoring, risk factors affecting the mood of the elderly can be found, and this will help with follow-up prevention work. Our study has shown that age, education, whether one lives alone, smoking, drinking, vegetable intake, long-term medication and BMI are significantly different in the elderly who have or do not have mood disorders. The results can provide us a micro-level understanding of the differences in mood disorders vis-à-vis the characteristics of elderly people in Taipei.

However, there are some limitations in this study. A major one is that the sample consisted of elderly participants who were willing to take part in the health examination; thus, the sampling was not randomized. The voluntary participants of this study may not be representative of the general population. Our cohort most likely represented a younger and more highly educated population, with more people having intact cognitive abilities. We might underestimate the risk from self-selection bias of healthier participants. Second, it was a second-hand dataset containing self-reported information, which may carry some bias, such as recall bias. Another limitation is that we only use BSRS-5 to estimate the outcome. Other measurements or clinical diagnosis could be added for more precise estimation in the future.

Our study provides a new approach for better understanding of mood disorders among the elderly with data from a long-term follow-up community health examination. Certain values of demographic variables, such as female gender, low education level and divorced; health behaviors of non-intake of milk, non-intake of fruit and vegetables, smoking and never exercising; and individual variables of long-term medication and a greater number of diseases were all associated with mood disorders. The findings of this research provide a macro view for understanding mood disorders as well as a micro view for identifying risk factors of mood disorders in the elderly through health examination. Our study suggests that health policy makers should focus on reducing those risk factors for developing mood disorders among the elderly.