Introduction

According to the definition from the World Health Organization (WHO), when the proportion of adults aged over 65 years old accounts for 20% of the total population, a society is defined as ‘super-aged’ [1]. It is estimated that by 2026, Taiwan's older adult population will result in a super-aged society; by then, there will be one older adult in every four to five people [2]. Moreover, physical activity levels decline and health status deteriorates with age, and some older adults with chronic illnesses may become inactive [3]. Some studies have shown that physical activity provides health benefits regardless of chronic disease [4]. Population aging is a global phenomenon, and older adults generally care most about their health [5]; from the perspective of preventive medicine, good exercise behavior of older adults plays an important role in achieving healthy behavior, and can also delay disease or disability associated with aging [6]. The World Health Organization has posited the problem of physical activity in the health behavior of older adults, mainly due to the lack of intensity and exercise time during exercise [7].

First of all, the so-called ‘health behavior’ refers to various activities performed to enhance physical fitness, maintain and promote physical and mental health, and avoid disease. It is the behavioral performance of an individual in a good state in terms of physical, psychological, and social adaptation [8,9]. Many scholars have conducted factor analysis on health behaviors [9-12], and the comprehensive results can be roughly divided into healthy eating behaviors, health care behaviors, emotional and stress management, physical activity, and changing behaviors that are harmful to health (such as the cessation of smoking and alcohol, gambling cessation) and five other categories. The definition of so-called exercise behavior, in a broad sense, refers to physical exercise performed by skeletal muscles that consume energy, including leisure time, housework, occupational work, etc. [13]. In a narrow sense, it refers to physical exercise performed during leisure time, and thus must include exercise intensity and exercise time [1]. In this study, the exercise behavior of older adults was defined according to the WHO. Older adults (over 65 years) should perform at least 150-300 minutes of moderate-intensity exercise per week, or at least 75-150 minutes of high-intensity exercise, or moderate-intensity and a combination of high-intensity activities and multiple days of physical activity on three or more days per week, emphasizing body balance and upper and lower extremity muscle strength training [1]. The promotion of exercise behavior is through understanding, control, and planning [14-16]; therefore, the primary motivation of this study was emphasize the importance of physical activity to older adults from practical actions through a multi-component exercise program, and then achieve autonomous exercise behavior. In addition, scholars have pointed out that factors such as gender, age, education level, marital status, smoking habits, drinking habits, and current living conditions all affect the exercise behaviors of older adults [17-21]. Therefore, these factors were included as background variables for the participants in this study. As for the factors that exercise behavior in-directly affects health behavior, evidence has shown that exercise behavior is positively correlated with healthy body weight and dietary intake [22-24]. Exercise is hypothesized to be inversely related to smoking and drinking [25]. In recent years, people have begun to pay attention to the mental health of different age groups. There is evidence that exercise can relieve stress [26], reduce anxiety levels [27], reduce the breadth and depth of depression [28], and promote mental health [29]. However, the functional de-cline in older adults and their spouses, and the high risk of cognitive decline, lack of social activities, and less support from group partners can easily induce the psychological pressure of loneliness and helplessness in older adults. Therefore, this study used a multi-sports intervention program (exercise behavior) to create opportunities for group sports (social support and social cohesion) [30], and determine its impact on the health behavior of older adults. Social cohesion is usually defined as a feeling of mutual trust, close connection, willingness to help each other, and shared values among members of a common group [31]. The level of social cohesion maintains the strength of a group connection. Nowadays, groups use social networks to form com-mon groups (such as Line groups, Facebook, Twitter, and Myspace), and share activity information with group members. When members are under high pressure, or when they are depressed, they will also provide support to their partners in the group, which is a manifestation of high cohesion [32,33]. Group activities are becoming more and more important in the social life of older adults, and people are paying more and more attention to maintaining the connection and cohesion of the living environment of older adults, which is also an important way to prevent loneliness [34]. Therefore, in-creasing social interaction and social cohesion through community activities, schools for older adults, volunteer service groups, and participation in religious activities can prevent loneliness and promote mental health [35]. In addition, studies have pointed out that if older adults have positive social cohesion, this has a positive impact on mental health and can reduce depression, hopelessness, and loneliness [36]. It has been reported that older adults with low social cohesion are more likely to have limited physical activity than those with high social cohesion [37], and older adults with low social cohesion may have physical disabilities which prevent them from participating in activities outside [38,39]; some older adults may be afraid that they will suffer injuries when going out [40]. Therefore, studying social cohesion in older adults is another motivation for promoting healthy behaviors in older adults.

Social support is broadly defined as a person being cared for and receiving assistance when needed [41], which is mainly provided by family members, close friends, and organizational members (such as community, neighborhood, or religious group) [42]. Older adults receive social support from significant others, and can receive emotional support (including a sense of belonging, empathy, and being valued), support for material assistance, and support for information feedback [43]. Older adults are likely to devote time and energy to close friends and family, who are the main sources of social support for older adults after they retire. Many studies have confirmed that social support for older adults is important for physical activity, diet, and adherence to medication [44-46], and that support from friends or significant others helps to avoid developing smoking and drinking habits [47,48], as well as participating in community activities or religious groups to meet friends of different ages [49], all of which are so-cially supported by older adults’ source and efficacy. Therefore, this study employed the factor of "social support" as another motivation in order to understand the benefits of health behaviors in older adults.

The psychological factors of older adults should be taken into consideration when exercising. If they are forced to exercise or are afraid of injury and do not have the confidence to do it, even if the exercise itself is effective, older adults will not persevere at all. Guiding older adults is therefore important for adherence to exercise [50]. Many scholars have put forward opinions on exercise compliance from different factors, including the content of each exercise, attendance rate [51], exercise continuity [52], exercise intensity persistence [52,53], etc. These factors can strengthen exercise effects, i.e., exercise compliance. Based on the above literature and the exercise compliance of older adults, this study adopted group activities and multi-component exercise pro-gram as an intervention measure, aiming to solve the following two problems: (1) Does a multi-component exercise program intervention help to improve the health behavior of older adults?; (2) Can exercise behavior, social cohesion, and social support effectively predict the health behavior of older adults after a multi-component exercise program intervention? Based on the research questions, the following hypotheses of this study were proposed. Hypothesis 1: multi-component exercise program interventions contribute to the promotion of healthy behaviors in older adults; Hypothesis 2: exercise behavior, social cohesion, and social support can effectively predict the health behaviors of older adults after the intervention of a multi-component exercise pro-gram.

Materials and Methods

This study used an original population (healthy older adults over 65 years old in Taipei City) to recruit; therefore, there was no random arrangement to be tested, and there was no control group for relative comparison would be directly affected by many variables. Therefore, the variables that might affect the experimental results in the experimental process were controlled and limited. The control variables were discussed in the article, so this type of experiment belongs to the one-group pretest-posttest de-sign [54].

Research Framework

The intervention measures in this study adopted a multi-component exercise program, which combined a variety of exercise elements and was taught by participating research members. In order to arouse the older adults' awareness and interest in exercise, in addition, this program adopted a group exercise method. In order to apply the effect of group strength, mutual encouragement, companionship, and proximity to a crowd to achieve cohesion and social opportunities for older adults, the research framework was established based on these viewpoints, as shown in Figure 1.

This study designed the “Exercise Behavior, Social Cohesion, and Social Support Scale” for older adults were designed and integrated into one questionnaire, and combined with the “Healthy Behavior Questionnaire for older adults, as well as an 8-week multi-component exercise program (walking, resistance training, and yoga) was implemented between the pre- and post-test of the two questionnaires. First, the differences in different background variables among older adults on exercise behavior, social cohesion, social support, and health behavior were explored. Finally, taking exercise behavior, social cohesion, and social support as factors, the explanatory power of the older adults' health behavior was determined.

Figure 1: Research structure.

Research Participants

In this study, older adults in Taipei City were used as the population. Through the poster promotion of the physical fitness center of the college (including a QR code link to the registration information, online registration, and a printed registration brochure placed in the community service center), 100 healthy older adults aged over 65 were recruited (50 males and 50 females); according to the WHO, healthy older adults are defined as those with the ability to maintain normal life functions and general cognitive level [55]. The average age of males was 76.57 ± 1.46 years old, and the average age of females was 74.73 ± 1.68 years old. There was no significant difference between the two by t test (t=-1.07, p<0.05), indicating that although the participants were of different genders, the age groups were homogeneous. All studies included monitoring, testing, data collection, and report writing by three researchers from the beginning to the end. The whole event was free, and each participant received a sports bottle for taking part (due to the free sign-up, those who could not participate due to environ-mental, identity, or social and economic constraints were excluded). In terms of the efficacy of the sample, the participants implemented a multi-component exercise pro-gram for 8 weeks (a total of two months). Classes were scheduled from Monday to Friday, 2 hours a day, including walking, resistance training, yoga, on-site teaching, and monitoring; Saturdays and Sundays were managed by the participants themselves. In terms of sample usage precision (using precision), the parameter mean differences (age) of the sample size were within the confidence interval, and the sample size was in line with the sample estimator for sports science research [56]. The recruited participants were first checked for personal background information, including gender, age, education level, marital status, smoking habits, drinking habits, and current living conditions (excluding those who were not affected by age, mental or physical conditions, or who were vulnerable to improper influence and coercion, or those who were unable to participate because of circumstances, status, or social and economic conditions, or those able to make decisions according to their own wishes). All participants in this study signed an informed consent form, which complied with scientific and ethical principles (contents included: no orthopedic disease or heart disease, no usual exercise program, and willingness to refrain from taking supplements that could in-crease muscle or sarcopenia during the study). This study was approved by the Jen-Ai Medical Foundation Dali Jen-Ai Hospital: Human Body Research Ethics Committee, approval number 110-96.

Research Materials

The interventions for the participants in this study were based on the Physical Activity Guidelines for Americans (PAGA) [57]; a multi-component exercise program (walking, resistance training, and yoga) was developed. Participants in this program experienced a total of 8 weeks [58-61]; an example of a one-week course is shown in Table 1. The type of exercise in the program was aerobic exercise, during which muscles used oxygen more efficiently and increased cardiac output [62,63]. The exercise intensity of the program was displayed in metabolic equivalent (MET). The MET is defined as the consumption of 3.5 milliliters of oxygen per kilogram of body weight per minute, which is roughly equivalent to a person sitting in a quiet state without any activity. An activity of 5 METs means that the consumption of oxygen per minute during exercise is 5 times that of rest [64]. This study implemented moderate exercise intensity, approximately 3.0-5.9 METs.

Table 1: Multi-component exercise training program.

Detection Method

Based on the above purpose, we aimed to solve the following two questions: (1) Does the multi-component exercise program help older adults improve health behavior? (2) After the intervention, how does the exercise behavior, social cohesion, and social support of older adults predict health behavior? Additionally, we proposed the following hypotheses according to the research questions. Hypothesis 1: multi-component exercise program contributes to promoting healthy behaviors in older adults; Hypothesis 2: after the intervention, the exercise behavior, social cohesion, and social support of older adults can effectively predict the health behavior of older adults. Therefore, the following detection content was designed.

Older Adults’ fitness test

We referred to the Senior Fitness Test (SFT) developed by Rikli and Jones [65], as showed in Table 2. The SFT test items are a 30 s sit-to-stand movement, 30 s dominant arm curl, 8-foot up-and-go (2.44 meters), 2 min step, and single leg (SL) exercises. This test is widely used to determine the physical fitness of older adults.

Table 2: Brief description of the Senior Fitness Test.

Exercise Behavior, Social Cohesion, and Social Support Questionnaire for older adults

The Exercise Behavior, Social Cohesion, and Social Support Questionnaire for older adults (EBSCSS) is divided into three scales; the first is a physical activity scale for older adults adapted from Ku, et al. This Chinese version of the scale is a short and easy-to-score survey assessing physical activity (such as leisure time, housework, and occupational work) performed over a 1-week period [13]. This study focused on the physical activities of older adults during their leisure time; therefore, "leisure time physical activity" was identified to revise and increase the exercise intensity and exercise time. In total, 5 questions were revised and evaluated with a five-point Likert scale [66], named the Exercise Behavior Inventory for older adults, as shown in Table 3.

Table 3: Contents of the exercise behavior inventory for older adults.

Second: The Social Cohesion Scale for Older Adults was adapted from Paramita, et al.'s study on social cohesion [67]. The participants in this study were older adults; thus, it was revised as Social Cohesion of Older Adults. In total, 10 questions were revised to evaluate each item using a five-point Likert scale [66], named the Social Cohesion Scale for older adults. Third: The social support scale for older adults was adapted from the perceived social support scale for older adults developed by Nazari, et al. [68], with a total of 7 questions revised into a five-point Likert scale assessment [66], named the Social Support Scale for older adults.

This study revised the Exercise Behavior, Social Cohesion, and Social Support Questionnaire for Older Adults. In the completed first draft of the scale, a sample of 150 senior students (average age 74.35 ± 2.17 years) from the Taiwan College of Per-forming Arts was obtained for a pre-test. On-site assistance from the research team was required to complete the instructions. The results of the pre-test retained all the items of the three scales after the project analysis, with 22 items and 3 factors in total. The reliability analysis of each factor was as follows: exercise behavior (5 items, explained variance of 26.85%, Cronbach’s α=0.79), social cohesion (10 items, explained variance of 31.17%, Cronbach’s α=0.83), social support (7 items, explained variance of 35.69%, Cronbach’s α=0.86). The scale had good reliability, with a Cronbach's alpha coefficient between 0.79 and 0.86. Participants rated each item on a five-point Likert scale with response intervals as follows: 1 = strongly disagree; 2 = slightly disagree; 3 = slightly agree; 4 = agree; 5 = strongly agree [66]. A higher total score indicated a higher frequency of implementation of the Exercise Behavior, Social Cohesion, and Social Support Questionnaire of the Older Adults.

Health Behavior Inventory for the older adults

This study modified the Health Behavior Inventory developed by Awabil and Anane, and the Health Protective Behavior Scale developed by Ping et al., both of which are methods of measuring health behaviors [69,70]. The two scales were revised and completed with a total of 36 items in the first draft in order to meet the needs of male and female of older adults in this study; this was named the Health Behavior Inventory (HBI) for older adults. The first draft of the scale was completed, and 150 senior students (average age 69.27±3.61 years) of the Taiwan College of Performing Arts were sampled for a pre-test; the research team assisted on-site in the senior classroom of the Taiwan College of Performing Arts to explain the study aims. The results of the pre-test retained 36 items after project analysis, which were divided into 5 factors. The reliability analysis of each factor is as follows: healthy diet (8 items, explained variance of 20.43%, Cronbach’s α = 0.83), medical and health care (8 items, explained variance of 17.37%, Cronbach’s α = 0.81), emotion and stress (8 items, explained variance of 15.62%, Cronbach’s α = 0.78), physical activity (7 items, explained variance of 25.19%, Cronbach’s α = 0.87), smoking and drinking (5 items, explained variance of 13.35%, Cronbach’s α = 0.71 ). The scale had good reliability, with a Cronbach's alpha coefficient of between 0.71 and 0.87. Participants rated each item on a five-point Likert scale with response intervals as follows: 1 = strongly disagree; 2 = slightly disagree; 3 = slightly agree; 4 = agree; 5 = strongly agree [66]. A higher total score indicated a higher frequency of implementing healthy behaviors.

Control Variable

This study only focused on healthy older adults over 65 years old. Gender and age were used as control variables in this study because these sociodemographic variables have been found to be related to health behaviors. A review of studies related to health behaviors showed that the gender and age of participants are variables that scholars need to control in research [71,72]. According to Selivanova and Cramm's research, health behaviors are significantly related to factors such as education level, marital status, smoking habits, drinking habits, and current living conditions [73]. In addition, before the experiment, the participants were educated about their diet and issued promotional health materials (including information on medical care, healthy diet, ways to relieve stress, the dangers of smoking and alcohol, and common sense of drug use), in order to reduce the interference during the experiment, which was the control variable in this study.

First, in order to confirm whether the sample size was sufficient, Cohen's d was used to determine the impact size of the t test [74]. Then, the size of Cohen's d effect was detected [75]. A Cohen's d value between 0.2 and 0.5 is a small effect; 0.5 to 0.8 is a medium effect; and a value higher than 0.8 is a large effect. The average age of males and females in this study had a small effect size, as shown by the Cohen’s d value of 0.281. All participants performed descriptive statistical analysis of background variables (including gender, age, education level, marital status, smoking habits, drinking habits, and current living conditions). Questionnaire surveys (EBSCSS and HBI) were also conducted to obtain pre- and post-test values of the Standard Deviation (SD) and mean (mean) [76]. Statistical t-test analyses were performed for the 30 s sit-to-stand, 30 s dominant arm curl, 8-foot up-and-go (2.44 meters), 2 min step, and single leg (SL) exercises for both males and females; the overall significance level was set to p<0.05, and the relationships between the factors of the two scales were compared using the Pear-son product difference correlation. Analysis of variance among the three factors (F test) and Durbin-Waston test (the test value is distributed between 0-4, the closer to 2, the greater the possibility that the observed values are independent of each other) [77]. Finally, multiple regression analysis was employed to determine the explanatory power of exercise behavior, social cohesion, and social support on the health behavior of older adults. The data in this study were analyzed using the SPSS 20.0 software (IBM^®, Armonk, NY, USA).

Results

Analysis of the Socio-Demographic Variables of Participants

The socio-demographic variables of the participants in this study-included gender, age, marital status, education level, smoking habits, drinking habits, and current living conditions, as shown in Table 4. Comparing males and females, there were no significant differences in mean age and marriage status. There was a significant difference in the education level between males and females. Most of the males were above high school level, whereas most of the females were at junior high school or primary school level. The smoking habits of males and females were significantly different, but among the current non-smokers and ex-smokers, 72% of males (50 of the older adults) and 96% of females (50 of the older adults). There was a significant difference in the drinking habits of males and females, although 24% of males (50) and 78% of females (50) did not currently drink alcohol. The drinking habits of males and females were mostly concentrated at once per month: males reached 52% (50 of the older adults) and females reached 20% (50 of the older adults). Regarding the current living conditions of males and females, most males and females lived with their spouse, followed by their children. In addition, the difference between males and females living alone was the largest; the proportion of males was higher than that of females.

Table 4: The socio-demographic characteristics of the sample.

Descriptive Statistics of Pre- and Post-tests of the SFT

The multi-component exercise program lasted for 8 weeks; the t-values and percentage differences between pre- and post-tests of the SFT for all participants showed in Table 5. This showed that both males and females improved in all items of the SFT. The study also found that the multi-component exercise program could help improve the balance ability, upper and lower limb muscle strength of older adults. The results presented in the discussion.

Table 5: Descriptive statistics of pre- and post-tests of the SFT in older adults.

Analysis of Participants' Pre- and Post-tests in the EBSCSS

The pre- and post-test values of the EBSCSS for all participants are shown in Table 6. It was found that after following the multi-component exercise program for 8 weeks, there were significant differences between males and females in exercise behavior, social cohesion, and social support. High levels of agreement were identified in the findings. There were no significant differences in the pre- and post-test values for questions 10-14, 19, 20, and 22 for each question. However, the average values of the pre-test and post-test results for males and females were consistent, reaching an acceptable conclusion.

Table 6: Pre- and post-test analyses of factors and items of the EBSCSS.

Analysis of Participants' Pre- and Post-test Values in the HBI

The pre- and post-test values of HBI for all participants in this study are shown in Table 7. It was found that after 8 weeks of the multi-component exercise program, most of the factors and items were significantly different; among them, females did not yield significant differences between pre- and post-tests of factor 5 and questions 18 to 20, indicating that female older adults had a fairly consistent response to avoiding tobacco, alcohol, and drug use; they all agreed very much. The factors of the HBI showed that the "regular physical activity" aspect had the largest difference between pre- and post-tests of males and females. Especially after 8 weeks of enacting the multi-component exercise program for female older adults, the 15th question (I exercise at least three days per week for more than 30 minutes each time) exhibited the largest difference between pre-test and post-test for all questions (t=18.12^*, p<0.05). The above results confirmed the acceptance of Hypothesis 1: a multi-component exercise program intervention could help the promotion of healthy behaviors in older adults.

Table 7: Pre- and post-test analyses of factors and items of the HBI.

Pearson Product-Moment Correlation Analysis of Various Post-test Factors between the EBSCSS and HBI

Each group in this study was on an equidistant scale; therefore, Pearson product-moment correlation analysis was used to assess post-test EBSCSS and HBI factors between males and females. Correlation analysis of post-test factors between the EBSCSS and HBI for males showed that the correlation coefficients were between 0.59 and 0.92, reaching a significant moderate or high correlation, as shown in Table 8. After 8 weeks of the multi-component exercise program, there was a positive medium–high correlation be-tween the EBSCSS and HBI. The correlation coefficient between the “exercise behavior” aspect of the EBSCSS and the “physical activity” aspect of the HBI was the highest, at 0.92 (p<0.05). The second highest correlation coefficient was between the “social sup-port” aspect of the EBSCSS and the “emotion and stress” aspect of the HBI: 0.91 (p<0.05).

Table 8: Correlation analysis of various post-test factors between the EBSCSS and HBI in males.

In the post-test correlation analysis between EBSCSS and HBI factors for females, the correlation coefficient was between 0.51 and 0.88, reaching a significant moderate or high correlation, as shown in Table 9. After 8 weeks of the multi-component exercise program, there was a positive medium-high correlation between the EBSCSS and HBI. Among them, the correlation coefficient between the “exercise behavior” aspect of the EBSCSS and the “physical activity” aspect of the HBI was the highest, at 0.88 (p<0.05). The second highest correlation coefficient was between the “social support” aspect of the EBSCSS and the “medical and healthcare” aspect of the HBI: 0.87 (p<0.05).

Table 9: Correlation analysis of various post-test factors between the EBSCSS and HBI in females.

The Explanatory Power of EBSCSS on Health Behavior

After the Pearson product-moment correlation analysis was performed, it was found that there was a high correlation between the EBSCSS and HBI factors in male and female older adults; then, the EBSCSS factors were used for the explanatory power of the dependent variable (HBI). In the regression analysis, the statistical significance of the regression pattern was tested first. There were significant differences in the results of the F-test between the dependent and independent variables of males and females: male F=87.354^* (p<0.01); female F = 95.173^* (p<0.01), and the Durbin-Waston test value was 1.613. Male and female respectively showed that the data of the three factors were independent. Finally, multiple regression was used to analyze the explanatory power of the forced entry method (the independent variable considered in the regression model) to the health behavior of the dependent variable.

The male EBSCSS results could explain the HBI to a significant level; the explained variation was R²= 8.4% (p<0.01). Among the independent variables, the estimated value of exercise behavior had the greatest impact on health behavior (β=0.716, p<0.01), followed by social support (β=0.625, p<0.01) and social cohesion (β=0.598, p< 0.01). The β-values of the three independent variables were all positive, indicating that the independent variables had a positive impact on the health behavior of male older adults, as shown in Table 10.

Table 10: Multiple linear regression analysis of males.

The explanatory power of the female EBSCSS results to the HBI reached a significant level; the explained variation was R² = 78.7% (p<0.01). Among the independent variables, the estimated value of exercise behavior had the greatest impact on health behavior (β = 0.743, p<0.01), followed by social cohesion (β =0.697, p<0.01) and social support (β = 0.641, p<0.01). The β-values of the three independent variables were all positive, indicating that the independent variables had a positive impact on the health behavior of female older adults, as shown in Table 11. Thus, Hypothesis 2 (exercise behavior, social cohesion, and social support after a multi-sport program intervention could effectively predict the health behavior of older adults) was validated and accepted.

Table 11: Multiple linear regression analysis of females.

Discussion

This study on socio-demographic variables in older adults found that there were significant differences between the educational level and health behaviors of males and females. According to Margolis’s research, it was indicated that well-educated, re-tired older adults may practice healthier behaviors, especially when they face new chronic diseases and start to make healthy behavior changes [78]; many studies have also confirmed that the level of education is related to practicing healthy behaviors [79-82]. The smoking habits of males and females were significantly different, which was consistent with many prior studies [73,83-85]; moreover, the majority of older smokers were male [86]. In this study, among the older adults who were non-smokers and ex-smokers, 72% were male and 96% were female. This phenomenon showed that the proportion of older female smokers was less, and male older adults might quit smoking because they had chronic diseases [84]. Studies have found that 76% of males and 22% of females have drinking habits; most older adults in Taiwan who drink alcohol approximately once per month are defined as practicing light drinking [87]. The results showed that most older adults reduce their time engaging in social opportunities in shopping malls after retirement (Taiwanese drinking culture is also regarded as a kind of social interaction), and focus more on the impact of physical health and chronic diseases. The gradual reduction in alcohol intake was consistent with many studies [88-91]. The current living status of older adults was mostly "living with spouse", followed by "living with spouse or children". However, the proportion of older adults living alone in Taiwan has tended to increase (24.6% of the elderly population over the age of 85 live alone; 22.4% of those aged 75 to 84 live alone; and 21.9% of people aged 65 to 74 live alone) [92], as found in many previous studies [93-95]. Conversely, many studies have shown that older adults living alone today should not be considered socially isolated or lonely and that health behaviors are not affected if older adults receive socially compensatory resources (such as support from friends, neighbors, or health care services) [96-98].

This study intervened with a multi-component exercise program, and found that the performance of male and female older adults in "30 s dominant arm curl", "30 s sit to stand", "2 min step", and "8-foot up-and-go" exercises had significantly improved, indicating that after 8 weeks of intervention, the extremity strength of the upper and lower limbs had improved significantly. There were also significant improvements in "single left leg" and "single right leg" stands, showing improved balance in the older adults. These findings were consistent with those of many other studies [99-104], also showing that improved lower extremity strength leads to neurological integrity and reduces the risk of fall in older adults. In addition, from the perspective of older adults’ adherence to exercise, after the implementation of the multi-component exercise pro-gram, it was found that the variety of exercises intrinsically motivated older adults to exercise, and the attendance rates of males and females reached 95% and 97%, respectively, demonstrating their exercise adherence [50,51]. The research team conducted exercise teaching and supervision, controlling the intensity of the exercise for the older adults when implementing the plan; thus, through group exercise, social support was engendered, such as increasing social opportunities and establishing mutual network communities. The fact that the exercise intervention virtually established adherence was a result, which was consistent with many studies [52,106-109].

After 8 weeks of the multi-component exercise program intervention for older adults, the Exercise Behavior, Social Cohesion, and Social Support (EBSCSS) questionnaire showed that there were significant differences between males and females’ pre- and post-test values for each factor. In terms of exercise behavior, older adults exercised outdoors 5-6 days per week for 2-3 hours each time; engaged in 90-120 minutes of moderately strenuous physical activity; and walked approximately 6000-8000 steps per day with an average walking rate of about 80 to 100 steps per minute, indicating moderate-intensity exercise. This result was in line with the WHO standard for physical activity in older adults [110]. In terms of social cohesion, older adults could increase their interactions with their neighbors by going outdoors, situate the community as central in their life, foster respect for the many different cultures in the community, gain the recognition of neighbors and friends, and have fixed groups (such as through religion and volunteer activities) to make many friends, and engage in volunteering to help others. This study showed that older adults develop their focus from community life, even if they live alone; they could also receive mental health benefits from com-munity interactions, such as religious or volunteer activities. This result is consistent with many prior studies [34,35,55,111,112]. In terms of social support, the results showed that older adults receive care, attention, and respect when they participate in-group activities, increasing social networks and reducing social isolation [113]. This was consistent with Shen et al.’s study, who encouraged older adults to participate in social activities and build good interpersonal relationships [114]. The presence of loved ones (such as a spouse or children) with older adults plays an important role in maintaining good mental health [115]. Numerous studies have confirmed that older men receive more support from their wives and report greater satisfaction [116,117]; more-over, older women receive feelings of well-being when they receive support from children or friends [118].

The HBI test results indicated that the older adults had reduced their use of tobacco, alcohol, and drugs. This result has been confirmed by several other studies; the alcohol and tobacco use rates of older adults were lower than those of young people [119,120], because most older adults’ perceived aging may be accompanied by chronic diseases; as such, the amount and frequency of alcohol consumption were both reduced [119]. In addition, the use of drugs by older adults has been prescribed by doc-tors [121,122], and most older adults are assisted by relatives or nurses when they see a doctor or take medication, ensuring that they can make good use of medical resources (Healthcare in Taiwan). Therefore, the "avoidance of tobacco, alcohol and drug use" and the "proper use of health care resources" in this study could be beneficial. In addition, it was worth paying attention to a "regular physical activity" and "healthy diet habit". The male and female older adults could exercise at least three days per week, for more than 30 minutes each time and mainly engaging in outdoor sports. This result was consistent with many prior studies [123-126]. Older adults could control the intake of fat, control the amount of salt, avoid eating a lot of sugar, eat no fried food, and control the amount of meat consumed in their diets. This result was consistent with many studies [127-131].

According to the correlation analysis of EBSCSS and HBI factors between males and females, it was shown that both males and females agreed that the correlation between exercise behavior (EBSCSS) and regular physical activity (HBI) was the high-est. Second, males rated "social support" (EBSCSS) as having the second highest correlation with "emotion and stress" (HBI). Females rated "social support" (EBSCSS) as having the second highest correlation with "medical and healthcare" (HBI). The multi-component exercise program intervention included a variety of exercise forms, which gave older adults a variety of different exercises to perform, each with different effects on the stimulation of body muscles. This exercise intervention had value. Evidence shows that physical activity has an impact on regular physical activity in older adults [132,133]. As for the relationship between social support, emotion, and stress, previous studies have shown that social support could lower blood pressure in older adults [134], and could enhance the ability to adapt to stress [135], which also showed that social support helps to relieve stress and reduce depression [136]. Social support is directly related to the medical care of older adults; community services could improve the physical and mental status of individuals and reduced the burden of care [137]. Asante et al.’s research confirmed that social support has a direct impact on older adults’ health care [138]; for example, older adults regard the doctor-patient relation-ship as helpful and trustworthy, and as such, older adults may be more willing to accept treatment [139,140].

According to the results of the regression analysis, the EBSCSS could effectively explain the health behavior of older adults: in 68.4% of males (R²=0.684) and 78.7% of females (R²=0.787). For both male and female older adults, exercise behavior had the greatest explanatory power on the health behavior of older adults, followed by social support and social cohesion, which had high predictive power on the health behavior of older adults. The effectiveness of these three factors in predicting health behavior has been confirmed in many studies. Lindsay Smith, et al. studied the relationship between social support and exercise in older adults, and found that people with higher social support, especially from family members, were more likely to have exercise behavior [141]. Social cohesion reduced the chances of taking up smoking, with positive effects on health behaviors [142-144]. It was also found that the stronger the social cohesion (sense of security and trust) of the older adults, the more likely they were to engage in exercise together, thus promoting health [126,145,146]. From the results of the group joint exercise, the older adults showed an active preference for exercising with others of the same age, as well as improved exercise compliance [147]. Kim et al. examine the importance of neighborhood social cohesion in promoting healthy behaviors in older adults [36]. Table 11 show that the three factors in this study could effectively predict health behaviors, which was consistent with many other related studies [146,148-153].

Although the current study has revealed the predictive power of exercise behavior, social support, and social cohesion on health behaviors in older adults, several limitations remain. This study used healthy older adults as participants, all of whom all lived in urban areas. The results of the study cannot be extrapolated to older adults with chronic diseases, mild disabilities, or rural areas. This study was a point-to-point horizontal study, and only three factors, i.e., exercise behavior, social support, and social cohesion, were used as predictive independent variables. Healthy older adults in urban communities generally have good dietary behaviors and awareness of medical care; therefore, diet and medical care were included as control variables, and only diet and the level of health education were assessed regarding these two variables. Finally, the multi-component exercise program in this study was only designed for healthy older adults; thus, the physical activity patterns of disabled or chronically ill older adults must be re-evaluated, and suitable physical activity programs should be designed for specific groups of people. Therefore, the applicability and generalizability of this exercise program was limited.

Conclusions

In this study, specially assigned staff guided the 8-week multi-component exercise program for older adults; tested the physical fitness of the older adults; and con-ducted the EBSCSS and HBI questionnaires for all participants. The results found that the multi-component exercise program interventions helped the older adults to per-form 30 s sit-to-stand, 30 s dominant arm curl, 8-foot up-and-go (2.44 meters), 2 min step, and single leg exercises. The program helped to improve upper and lower extremity muscle strength and enhance balance, with the beneficial effect of preventing the risk of fall in older adults. Exercise interventions positively affect exercise behavior, social support, and social cohesion in older adults, with positive benefits on health behaviors. It has been suggested that the physical activity of older adults should be set at a moderate intensity, and group exercise should be adopted outdoors, so as to establish social networking opportunities, which are beneficial to mental health, and then to improve physical and mental health. In the future, a diverse group exercise program should be established for older adults with different physical and mental conditions to improve mental health and increase social participation, thereby improving the chances of successful and healthy aging.

Conflicts of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest.

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki and was approved by a local Institutional Review Board (protocol code 110-96).

Informed Consent Statement: Informed consent was obtained from all the participants in the study.

Data Availability Statement

The experimental results obtained real data about study participants pre- and post-tests. Participants agreed with the data collection procedure via an informed con-sent form; examples of the informed consent forms can be disclose upon reasonable request. All datasets on which the conclusions of the paper rely are available to editors, reviewers, and readers.

Acknowledgments

The authors appreciate the staff of the fitness center of the National Taiwan College of Performing Arts for providing us with the opportunity and facility to research the topic. Moreover, we thank all of the participants for actively participating in the experimental courses and adhering to the independent exercise regime. Finally, we would like to thank the Jen-Ai Medical Foundation Dali Jen-Ai Hospital for reviewing the ethics-related details of this study.

Author Contributions

Conceptualization, W.K.C., H.H., and W.C.E.; resistance training program and methodology, W.K.C. and H.H.; software, W.C.E.; validation, W.K.C. and W.C.E.; formal analysis, W.K.C.; investigation, W.K.C.; resources H.H.; data curation, W.K.C. and W.C.E.; writing—original draft preparation, W.K.C.; writing-review and editing, H.H., W.K.C., and W.C.E.; supervision, W.K.C.; project administration, W.K.C. All authors have read and agreed to the published version of the manuscript.

References

1. WHO (2014) World population ageing.
2. Lin YY, Huang CS (2016) Aging in Taiwan: Building a Society for Active Aging and Aging in Place. The Gerontologist 56: 176-183.
3. Cunningham C, O’Sullivan R, Caserotti P, Tully MA (2020) Consequences of physical inactivity in older adults: A systematic review of reviews and meta-analyses. Scand J Med Sci Sports 30: 816-827.
4. Russell E (2013) Exercise is medicine. CMAJ 185: E526.
5. Rosenberg M, Tomioka S, Barber SL (2022) Research to inform health systems' responses to rapid population ageing: a collection of studies funded by the WHO Centre for Health Development in Kobe, Japan. Health Res Policy Syst 20: 128.
6. McPhee JS, French DP, Jackson D, Nazroo J, Pendleton N, et al. (2016) Physical activity in older age: perspectives for healthy ageing and frailty. Biogerontology 17: 567-580.
7. WHO (2020) WHO guidelines on physical activity and sedentary behaviour.
8. Short SE, Mollborn S (2015) Social Determinants and Health Behaviors: Conceptual Frames and Empirical Advances. Curr Opin Psychol 5: 78-84.
9. Havigerová JM, Dosedlová J, Burešová I (2019) One health behavior or many health-related behaviors? Psychol Res Behav Manag 12: 23-30.
10. Cook CR, Davis C, Brown EC, Locke J, Ehrhart MG, et al. (2018) Confirmatory factor analysis of the Evidence-Based Practice Attitudes Scale with school-based behavioral health consultants. Implement Sci 13: 116.
11. Chawłowska E, Staszewski R, Jóźwiak P, Lipiak A, Zawiejska A (2022) Development and Validation of a Health Behaviour Scale: Exploratory Factor Analysis on Data from a Multicentre Study in Female Primary Care Patients. Behav Sci 12: 378.
12. Qiu Q, Dai S, Yan J (2022) Health behaviors of late adolescents in China: Scale development and preliminary validation. Front Psychol 13: 1004364.
13. Ku PW, Sun W, Chen LJ (2013) Reliability and Validity of the Chinese Version of the Physical Activity Scale for the Elderly. Sport & Exercise Research 15: 309-319.
14. Huang WY, Wu CE (2021) Predict the exercise behavior intention of the older adults in Taipei City to promote exercise behavior. Sci Prog 104: 368504211042468.
15. Sáez de Asteasu ML, Martínez-Velilla N, Zambom-Ferraresi F, Casas-Herrero Á, Izquierdo M (2017) Role of physical exercise on cognitive function in healthy older adults: A systematic review of randomized clinical trials. Ageing Res Rev 37: 117-134.
16. Quigley A, MacKay-Lyons M, Eskes G (2020) Effects of Exercise on Cognitive Performance in Older Adults: A Narrative Review of the Evidence, Possible Biological Mechanisms, and Recommendations for Exercise Prescription. J Aging Res 2020: 1407896.
17. Boyette LW, Lloyd A, Boyette JE, Watkins E, Furbush L, et al. (2002) Personal characteristics that influence exercise behavior of older adults. J Rehabil Res Dev 39: 95-103.
18. Janke M, Davey A, Kleiber D (2006) Modeling Change in Older Adults' Leisure Activities. Leisure Sciences 28: 285-303.
19. Pettee KK, Brach JS, Kriska AM, Boudreau R, Richardson CR, et al. (2006) Influence of marital status on physical activity levels among older adults. Med Sci Sports Exerc 38: 541-546.
20. Park CH, Elavsky S, Koo KM (2014) Factors influencing physical activity in older adults. J Exerc Rehabil 10: 45-52.
21. McKee G, Kearney PM, Kenny RA (2015) The factors associated with self-reported physical activity in older adults living in the community. Age Ageing 44: 586-592.
22. Psouni L, Hassandra M, Theodorakis Y (2016) Exercise and Healthy Eating Intentions and Behaviors among Normal Weight and Overweight/Obese Adults. Psychology 7: 598-611.
23. Samdal GB, Eide GE, Barth T, Williams G, Meland E (2017) Effective behaviour change techniques for physical activity and healthy eating in overweight and obese adults; systematic review and meta-regression analyses. Int J Behav Nutr Phys Act 14: 42.
24. Huang JH, Li RH, Huang SL, Sia HK, Hsu WT, et al. (2019) Health-Associated Nutrition and Exercise Behaviors in Relation to Metabolic Risk Factors Stratified by Body Mass Index. Int J Environ Res Public Health 16: 869.
25. Johnson MF, Nichols JF, Sallis JF, Calfas KJ, Hovell MF (1998) Interrelationships between physical activity and other health behaviors among university women and men. Prev Med 27: 536-544.
26. Zhang Y, Zhang H, Ma X, Di Q (2020) Mental Health Problems during the COVID-19 Pandemics and the Mitigation Effects of Exercise: A Longitudinal Study of College Students in China. Int J Environ Res Public Health 17: 3722.
27. Cho D, Kim SH (2020) Health Capability and Psychological Effects of Regular Exercise on Adults: Middle-Aged and Older. Int J Aging Hum Dev 91: 520-537.
28. Huang X, Wang Y, Zhang H (2023) Effects of physical exercise intervention on depressive and anxious moods of college students: A meta-analysis of randomized controlled trials. Asian Journal of Sport and Exercise Psychology.
29. Wang P, Wang J, Yuan X, Yang S, Wang X, et al. (2022) The Relationship between Exercise Behavior and Mental Health during the COVID-19 Epidemic: Research Based on the Weibo Exercise Behavior User Dictionary. Curr Psychol 1-14.
30. Picorelli AM, Pereira LS, Pereira DS, Felício D, Sherrington C (2014) Adherence to exercise programs for older people is influenced by program characteristics and personal factors: a systematic review. J Physiother 60: 151-156.
31. Cagney KA, Glass TA, Skarupski KA, Barnes LL, Schwartz BS, et al. (2009) Neighborhood-level cohesion and disorder: measurement and validation in two older adult urban populations. J Gerontol B Psychol Sci Soc Sci 64: 415-424.
32. Kemperman A, van den Berg P, Weijs-Perrée M, Uijtdewillegen K (2019) Loneliness of Older Adults: Social Network and the Living Environment. Int J Environ Res Public Health 16: 406.
33. Benvenuti M, Giovagnoli S, Mazzoni E, Cipresso P, Pedroli E, et al. (2020) The Relevance of Online Social Relationships Among the Elderly: How Using the Web Could Enhance Quality of Life? Front Psychol 11: 551862.
34. Weijs-Perrée M, van den Berg P, Arentze T, Kemperman A (2015) Factors influencing social satisfaction and loneliness: a path analysis. Journal of Transport Geography 45: 24-31.
35. van den Berg P, Sanders J, Maussen SJE, Kemperman A (2021) Collective self-build for senior friendly communities. Studying the effects on social cohesion, social satisfaction and loneliness. Housing Studies 2021: 1941793.
36. Kim ES, Chen Y, Kawachi I, VanderWeele TJ (2020) Perceived neighborhood social cohesion and subsequent health and well-being in older adults: An outcome-wide longitudinal approach. Health Place 66: 102420.
37. Nguyen TT, Rist PM, Glymour MM (2016) Are self-reported neighbourhood characteristics associated with onset of functional limitations in older adults with or without memory impairment? J Epidemiol Community Health 70: 1017-1023.
38. Latham K, Clarke PJ (2018) Neighborhood Disorder, Perceived Social Cohesion, and Social Participation Among Older Americans: Findings From the National Health & Aging Trends Study. J Aging Health 30: 3-26.
39. Millar RJ (2020) Neighborhood Cohesion, Disorder, and Physical Function in Older Adults: An Examination of Racial/Ethnic Differences. J Aging Health 32: 1133-1144.
40. Latham K, Williams MM (2015) Does Neighborhood Disorder Predict Recovery from Mobility Limitation? Findings from the Health and Retirement Study. J Aging Health 27: 1415-1442.
41. Drageset J (2021) Social Support. In: Health Promotion in Health Care-Vital Theories and Research. Haugan G, Eriksson M (Eds.). Springer. Pp: 137-144.
42. Zhang Y, Yeager VA, Hou S (2018) The Impact of Community-Based Supports and Services on Quality of Life Among the Elderly in China: A Longitudinal Study. J Appl Gerontol 37: 1244-1269.
43. Uchino BN, Bowen K, Kent de Grey R, Mikel J, Fisher EB (2018) Social Support and Physical Health: Models, Mechanisms, and Opportunities. In: Principles and Concepts of Behavioral Medicine: A Global Handbook. Fisher EB, Cameron LD, Christensen AJ, Ehlert U, Guo Y, et al. (Eds.). Springer New York: pp. 341-372.
44. Kim CJ, Schlenk EA, Kim DJ, Kim M, Erlen JA, et al. (2015) The role of social support on the relationship of depressive symptoms to medication adherence and self-care activities in adults with type 2 diabetes. J Adv Nurs 71: 2164-2175.
45. Mendes R, Martins S, Fernandes L (2019) Adherence to Medication, Physical Activity and Diet in Older Adults With Diabetes: Its Association With Cognition, Anxiety and Depression. J Clin Med Res 11: 583-592.
46. Laiou E, Rapti I, Markozannes G, Cianferotti L, Fleig L, et al. (2020) Social support, adherence to Mediterranean diet and physical activity in adults: results from a community-based cross-sectional study. J Nutr Sci 9: e53.
47. Yun EH, Kang YH, Lim MK, Oh JK, Son JM (2010) The role of social support and social networks in smoking behavior among middle and older aged people in rural areas of South Korea: a cross-sectional study. BMC Public Health 10: 78.
48. Kwon DM, Santiago-Torres M, Mull KE, Sullivan BM, Bricker JB (2022) Older adults who smoke: Do they engage with and benefit from web-based smoking cessation interventions? Prev Med 161: 107118.
49. Coleman PG, Carare RO, Petrov I, Forbes E, Saigal A, et al. (2011) Spiritual belief, social support, physical functioning and depression among older people in Bulgaria and Romania. Aging Ment Health 15: 327-333.
50. Hawley-Hague H, Horne M, Campbell M, Demack S, Skelton DA, et al. (2014) Multiple levels of influence on older adults' attendance and adherence to community exercise classes. Gerontologist 54: 599-610.
51. Sullivan-Marx EM, Mangione KK, Ackerson T, Sidorov I, Maislin G, et al. (2011) Recruitment and retention strategies among older African American women enrolled in an exercise study at a PACE program. Gerontologist 51: S73-S81.
52. Hawley-Hague H, Horne M, Skelton DA, Todd C (2016) Review of how we should define (and measure) adherence in studies examining older adults' participation in exercise classes. BMJ Open 6: e011560.
53. Dorgo S, King GA, Candelaria NG, Bader JO, Brickey GD, et al. (2009) Effects of manual resistance training on fitness in adolescents. J Strength Cond Res 23: 2287-2294.
54. Reichardt CS, Little TD (2019) Quasi-experimentation: A guide to design and analysis. The Guilford Press.
55. Rudnicka E, Napierała P, Podfigurna A, Męczekalski B, Smolarczyk R, et al. (2020) The World Health Organization (WHO) approach to healthy ageing. Maturitas 139: 6-11.
56. Abt G, Boreham C, Davison G, Jackson R, Nevill A, et al. (2020) Power, precision, and sample size estimation in sport and exercise science research. J Sports Sci 38: 1933-1935.
57. Piercy KL, Troiano RP, Ballard RM, Carlson SA, Fulton JE, et al. (2018) The Physical Activity Guidelines for Americans. JAMA 320: 2020-2028.
58. Amini M, Mirmoezzi M, Salmanpour M, Khorshidi D (2018) Effect of 8-Week of Selected Aerobic Exercises on Improving the Quality of Life in Healthy Aged Sedentary Men. International journal of Sport Studies for Health 2018: e67514.
59. Baker BS, Weitzel KJ, Royse LA, Miller K, Guess TM, et al. (2021) Efficacy of an 8-Week Resistance Training Program in Older Adults: A Randomized Controlled Trial. J Aging Phys Act 29: 121-129.
60. Pepera G, Krinta K, Mpea C, Antoniou V, Peristeropoulos A, et al. (2022) Randomized Controlled Trial of Group Exercise Intervention for Fall Risk Factors Reduction in Nursing Home Residents. Can J Aging 1-9.
61. McCormack GR, Petersen J, Ghoneim D, Blackstaffe A, Naish C, et al. (2022) Effectiveness of an 8-Week Physical Activity Intervention Involving Wearable Activity Trackers and an eHealth App: Mixed Methods Study. JMIR Form Res 6: e37348.
62. Harveson AT, Hannon JC, Brusseau TA, Podlog L, Papadopoulos C, et al. (2016) Acute Effects of 30 Minutes Resistance and Aerobic Exercise on Cognition in a High School Sample. Res Q Exerc Sport 87: 214-220.
63. Broskey NT, Martin CK, Burton JH, Church TS, Ravussin E, et al. (2021) Redman, L.M. Effect of Aerobic Exercise-induced Weight Loss on the Components of Daily Energy Expenditure. Med Sci Sports Exerc 53: 2164-2172.
64. Hernando C, Hernando C, Martinez-Navarro I, Collado-Boira E, Panizo N, et al. (2020) Estimation of energy consumed by middle-aged recreational marathoners during a marathon using accelerometry-based devices. Sci Rep 10: 1523.
65. Rikli RE, Jones CJ (2013) Development and validation of criterion-referenced clinically relevant fitness standards for maintaining physical independence in later years. Gerontologist 53: 255-267.
66. Joshi A, Kale S, Chandel S, Pal D (2015) Likert Scale: Explored and Explained. British Journal of Applied Science & Technology 7: 396-403.
67. Paramita SA, Yamazaki C, Hilfi L, Sunjaya DK, Koyama H (2021) Social cohesion and quality of life in Bandung: A cross sectional study. PLoS One 16: e0258472.
68. Nazari S, Afshar PF, Sadeghmoghadam L, Shabestari AN, Farhadi A (2020) Developing the perceived social support scale for older adults: A mixed-method study. AIMS Public Health 7: 66-80.
69. Awabil G, Anane E (2018) The Health Behaviour Inventory: Initial Development, Factor Structure and Evidence of Reliability. Journal of Educational and Social Research 8: 45-51.
70. Ping W, Cao W, Tan H, Guo C, Dou Z, et al. (2018) Health protective behavior scale: Development and psychometric evaluation. PLoS One 13: e0190390.
71. Nielsen MW, Stefanick ML, Peragine D, Neilands TB, Ioannidis JPA, et al. (2021) Gender-related variables for health research. Biol Sex Differ 12: 23.
72. Bartram D (2020) Age and Life Satisfaction: Getting Control Variables under Control. Sociology 55: 421-437.
73. Selivanova A, Cramm JM (2014) The relationship between healthy behaviors and health outcomes among older adults in Russia. BMC Public Health 14: 1183.
74. Cohen J (1988) Statistical Power Analysis for the Behavioral Sciences (2^nd Edition). Hillsdale, NJ: Lawrence Erlbaum Associates, Publishers.
75. Team J (2022) Amsterdam, The Netherlands; Version 0.12.2 computer software.
76. Kelley K, Rausch JR (2006) Sample size planning for the standardized mean difference: accuracy in parameter estimation via narrow confidence intervals. Psychol Methods 11: 363-385.
77. Durbin J, Watson GS (1951) Testing for serial correlation in least squares regression. II. Biometrika 38: 159-178.
78. Margolis R (2013) Educational differences in healthy behavior changes and adherence among middle-aged Americans. J Health Soc Behav 54: 353-368.
79. Ross CE, Wu CL (1996) Education, age, and the cumulative advantage in health. J Health Soc Behav 37: 104-120.
80. Lai YH (2018) The Effect of Education Level on Health Behavior and Mortality of the Elderly in Taiwan. 282-285.
81. Belo P, Navarro-Pardo E, Pocinho R, Carrana P, Margarido C (2020) Relationship Between Mental Health and the Education Level in Elderly People: Mediation of Leisure Attitude. Front Psychol 11: 573.
82. Long C, Liu P, Yi C (2020) Does Educational Attainment Affect Residents' Health? Healthcare (Basel) 8: 364.
83. Peixoto SV, Firmo JO, Lima-Costa MF (2005) Factors associated to smoking habit among older adults (The Bambuí Health and Aging Study). Rev Saude Publica 39: 746-753.
84. Oktaviani LW, Hsu HC, Chen YC (2022) Effects of Health-Related Behaviors and Changes on Successful Aging among Indonesian Older People. Int J Environ Res Public Health 19: 5952.
85. Tian WH, Tien JJ (2020) Health Behaviors and Health Status among Middle-Aged and Older Adults with Chronic Diseases in Taiwan. Int J Environ Res Public Health 17: 7196.
86. Jeong K, Cho S, Ryu J, Cho HJ, Kim S (2022) Effects of Changes in Smoking Behavior of Older Adults' Oral Health. Healthcare (Basel) 10: 2127.
87. Balsa AI, Homer JF, Fleming MF, French MT (2008) Alcohol consumption and health among elders. Gerontologist 48: 622-636.
88. Kelly S, Olanrewaju O, Cowan A, Brayne C, Lafortune L (2018) Alcohol and older people: A systematic review of barriers, facilitators and context of drinking in older people and implications for intervention design. PLoS One 13: e0191189.
89. Griswold M, Fullman N, Hawley C, Arian N, Zimsen S, et al. (2016) Alcohol use and burden for 195 countries and territories, 1990-2016: A systematic analysis for the Global Burden of Disease Study 2016. Lancet 392: 1015-1035.
90. Bareham BK, Kaner E, Spencer LP, Hanratty B (2019) Drinking in later life: a systematic review and thematic synthesis of qualitative studies exploring older people's perceptions and experiences. Age Ageing 48: 134-146.
91. Boumans J, van de Mheen D, Crutzen R, Dupont H, Bovens R, et al. (2022) Understanding How and Why Alcohol Interventions Prevent and Reduce Problematic Alcohol Consumption among Older Adults: A Systematic Review. Int J Environ Res Public Health 19: 3188.
92. Ministry of Health and Welfare, Taiwan (2022) List of Social Welfare Official Statistics.
93. Henning-Smith C (2016) Quality of Life and Psychological Distress Among Older Adults: The Role of Living Arrangements. J Appl Gerontol 35: 39-61.
94. Saito T, Murata C, Aida J, Kondo K (2017) Cohort study on living arrangements of older men and women and risk for basic activities of daily living disability: findings from the AGES project. BMC Geriatr 17: 183.
95. Bolina AF, Araújo MDC, Haas VJ, Tavares D (2021) Association between living arrangement and quality of life for older adults in the community. Rev Lat Am Enfermagem 29: e3401.
96. Klinenberg E (2016) Social Isolation, Loneliness, and Living Alone: Identifying the Risks for Public Health. Am J Public Health 106: 786-787.
97. Zhou Z, Mao F, Ma J, Hao S, Qian ZM, et al. (2018) A Longitudinal Analysis of the Association between Living Arrangements and Health Among Older Adults in China. Res Aging 40: 72-97.
98. Lee S (2022) Does Living Alone Affect Self-Perceptions of Aging? Findings From Two Waves of the Health and Retirement Study. Gerontol Geriatr Med 8: 23337214221077798.
99. Toraman NF, Erman A, Agyar E (2004) Effects of multicomponent training on functional fitness in older adults. J Aging Phys Act 12: 538-553.
100. Lee IH, Park SY (2013) Balance improvement by strength training for the elderly. J Phys Ther Sci 25: 1591-1593.
101. Eckardt N (2016) Lower-extremity resistance training on unstable surfaces improves proxies of muscle strength, power and balance in healthy older adults: a randomised control trial. BMC Geriatr 16: 191.
102. González-Ravé JM, Cuéllar-Cañadilla R, García-Pastor T, Juárez Santos-García D (2020) Strength Improvements of Different 10-Week Multicomponent Exercise Programs in Elderly Women. Front Public Health 8: 130.
103. Huang WY, Wu CE (2022) Interventions to Improve Body Composition, Upper and Lower Extremity Muscle Strength, and Balance Ability of Older Female Adults: An Intervention Study. Int J Environ Res Public Health 19: 4765.
104. Sanchez-Lastra MA, Varela S, Cancela JM, Ayán C (2022) Upper versus lower body resistance exercise with elastic bands: effects on cognitive and physical function of institutionalized older adults. Eur Geriatr Med 13: 907-916.
105. Picorelli AMA, Pereira LSM, Pereira DS, Felício D, Sherrington C (2014) Adherence to exercise programs for older people is influenced by program characteristics and personal factors: a systematic review. J Physiother 60: 151-156.
106. Room J, Hannink E, Dawes H, Barker K (2017) What interventions are used to improve exercise adherence in older people and what behavioural techniques are they based on? A systematic review. BMJ Open 7: e019221.
107. Valenzuela T, Okubo Y, Woodbury A, Lord SR, Delbaere K (2018) Adherence to Technology-Based Exercise Programs in Older Adults: A Systematic Review. J Geriatr Phys Ther 41: 49-61.
108. Rivera-Torres S, Fahey TD, Rivera MA (2019) Adherence to Exercise Programs in Older Adults: Informative Report. Gerontol Geriatr Med 5: 2333721418823604.
109. Shaw JF, Pilon S, Vierula M, McIsaac DI (2022) Predictors of adherence to prescribed exercise programs for older adults with medical or surgical indications for exercise: a systematic review. Syst Rev 11: 80.
110. WHO (2022) Physical activity.
111. Kim J, Kim J, Han A (2020) Leisure Time Physical Activity Mediates the Relationship Between Neighborhood Social Cohesion and Mental Health Among Older Adults. J Appl Gerontol 39: 292-300.
112. Choi NG, Kim J, DiNitto DM, Marti CN (2015) Perceived Social Cohesion, Frequency of Going Out, and Depressive Symptoms in Older Adults: Examination of Longitudinal Relationships. Gerontol Geriatr Med 1: 2333721415615478.
113. Czaja SJ, Moxley JH, Rogers WA (2021) Social Support, Isolation, Loneliness, and Health Among Older Adults in the PRISM Randomized Controlled Trial. Front Psychol 12: 728658.
114. Shen T, Li D, Hu Z, Li J, Wei X (2022) The impact of social support on the quality of life among older adults in China: An empirical study based on the 2020 CFPS. Front Public Health 10: 914707.
115. Son H, Cho HJ, Cho S, Ryu J, Kim S (2022) The Moderating Effect of Social Support between Loneliness and Depression: Differences between the Young-Old and the Old-Old. Int J Environ Res Public Health 19: 2322.
116. Mahmud MA, Hazrin M, Muhammad EN, Mohd Hisyam MF, Awaludin SM, et al. (2020) Social support among older adults in Malaysia. Geriatr Gerontol Int 20: 63-67.
117. Ermer AE, Proulx CM (2019) Social support and well-being among older adult married couples: A dyadic perspective. Journal of Social and Personal Relationships 37: 1073-1091.
118. Thomas PA (2010) Is It Better to Give or to Receive? Social Support and the Well-being of Older Adults. J Gerontol B Psychol Sci Soc Sci 65B: 351-357.
119. Kuerbis A, Sacco P, Blazer DG, Moore AA (2014) Substance abuse among older adults. Clin Geriatr Med 30: 629-654.
120. Moos RH, Schutte KK, Brennan PL, Moos BS (2009) Older adults' alcohol consumption and late-life drinking problems: a 20-year perspective. Addiction 104: 1293-1302.
121. Pohontsch NJ, Heser K, Löffler A, Haenisch B, Parker D, et al. (2017) General practitioners' views on (long-term) prescription and use of problematic and potentially inappropriate medication for oldest-old patients-A qualitative interview study with GPs (CIM-TRIAD study). BMC Fam Pract 18: 22.
122. Mortazavi SS, Shati M, Malakouti SK, Khankeh HR, Mehravaran S, et al. (2019) Physicians' role in the development of inappropriate polypharmacy among older adults in Iran: a qualitative study. BMJ Open 9: e024128.
123. Crane BM, Moored KD, Rosso AL, Carlson MC (2022) Using GPS Technologies to Examine Community Mobility in Older Adults. J Gerontol A Biol Sci Med Sci glac 185.
124. Levinger P, Sales M, Polman R, Haines T, Dow B, et al. (2018) Outdoor physical activity for older people-the senior exercise park: Current research, challenges and future directions. Health Promot J Austr 29: 353-359.
125. Yang YJ (2019) An Overview of Current Physical Activity Recommendations in Primary Care. Korean J Fam Med 40: 135-142.
126. Creighton RM, Paradis KF, Blackburn NE, Tully MA (2022) Group-Based Physical Activity Interventions Targeting Enjoyment in Older Adults: A Systematic Review. J Ageing Longev 2: 113-129.
127. Govindaraju T, Sahle BW, McCaffrey TA, McNeil JJ, Owen AJ (2018) Dietary Patterns and Quality of Life in Older Adults: A Systematic Review. Nutrients 10: 971.
128. Gu Q, Sable CM, Brooks-Wilson A, Murphy RA (2020) Dietary patterns in the healthy oldest old in the healthy aging study and the Canadian longitudinal study of aging: a cohort study. BMC Geriatr 20: 106.
129. Fong BYF, Chiu WK, Chan WFM, Lam TY (2021) A Review Study of a Green Diet and Healthy Ageing. Int J Environ Res Public Health 18: 8024.
130. Dominguez LJ, Veronese N, Baiamonte E, Guarrera M, Parisi A, et al. (2022) Healthy Aging and Dietary Patterns. Nutrients 14: 889.
131. Casas R, Ribó-Coll M, Ros E, Fitó M, Lamuela-Raventos RM, et al. (2022) Change to a healthy diet in people over 70 years old: the PREDIMED experience. Eur J Nutr 61: 1429-1444.
132. Pinheiro MB, Oliveira JS, Baldwin JN, Hassett L, Costa N, et al. (2022) Impact of physical activity programs and services for older adults: a rapid review. Int J Behav Nutr Phys Act 19: 87.
133. Parra-Rizo MA, Vásquez-Gómez J, Álvarez C, Diaz-Martínez X, Troncoso C, et al. (2022) Predictors of the Level of Physical Activity in Physically Active Older People. Behav Sci (Basel) 12: 331.
134. Birditt KS, Newton N, Hope S (2014) Implications of marital/partner relationship quality and perceived stress for blood pressure among older adults. J Gerontol B Psychol Sci Soc Sci 69: 188-198.
135. Ozbay F, Johnson DC, Dimoulas E, Morgan CA, Charney D, et al. (2007) Social support and resilience to stress: from neurobiology to clinical practice. Psychiatry (Edgmont) 4: 35-40.
136. Sherman SM, Cheng YP, Fingerman KL, Schnyer DM (2016) Social support, stress and the aging brain. Soc Cogn Affect Neurosci 11: 1050-1058.
137. Dai Y, Zhang CY, Zhang BQ, Li Z, Jiang C, et al. (2016) Social support and the self-rated health of older people: A comparative study in Tainan Taiwan and Fuzhou Fujian province. Medicine 2016 95: e3881.
138. Asante S, Karikari G (2022) Social Relationships and the Health of Older Adults: An Examination of Social Connectedness and Perceived Social Support. J Ageing Longev 2: 49-62.
139. Fuertes JN, Mislowack A, Bennett J, Paul L, Gilbert TC, et al. (2007) The physician-patient working alliance. Patient Educ Couns 66: 29-36.
140. Reblin M, Uchino BN (2008) Social and emotional support and its implication for health. Curr Opin Psychiatry 21: 201-205.
141. Smith GL, Banting L, Eime R, O'Sullivan G, van Uffelen JGZ (2017) The association between social support and physical activity in older adults: a systematic review. Int J Behav Nutr Phys Act 14: 56.
142. Patterson JM, Eberly LE, Ding Y, Hargreaves M (2004) Associations of smoking prevalence with individual and area level social cohesion. J Epidemiol Community Health 58: 692-697.
143. Fleischer NL, Lozano P, Santillán EA, Shigematsu LMR, Thrasher JF (2015) The impact of neighbourhood violence and social cohesion on smoking behaviours among a cohort of smokers in Mexico. J Epidemiol Community Health 69: 1083-1090.
144. Alcalá HE, Sharif MZ, Albert SL (2016) Social cohesion and the smoking behaviors of adults living with children. Addict Behav 53: 201-205.
145. de Leon CFM, Cagney KA, Bienias JL, Barnes LL, Skarupski KA, et al. (2009) Neighborhood social cohesion and disorder in relation to walking in community-dwelling older adults: a multilevel analysis. J Aging Health 21: 155-171.
146. Chen S, Sun Y, Seo BK (2022) The Effects of Public Open Space on Older People's Well-Being: From Neighborhood Social Cohesion to Place Dependence. Int J Environ Res Public Health 19: 16170.
147. Beauchamp MR, Ruissen GR, Dunlop WL, Estabrooks PA, Harden SM, et al. (2018) Group-based physical activity for older adults (GOAL) randomized controlled trial: Exercise adherence outcomes. Health Psychol 37: 451-461.
148. Resnick B, Orwig D, Magaziner J, Wynne C (2002) The effect of social support on exercise behavior in older adults. Clin Nurs Res 11: 52-70.
149. Christensen U, Schmidt L, Budtz-Jørgensen E, Avlund K (2006) Group cohesion and social support in exercise classes: results from a danish intervention study. Health Educ Behav 33: 677-689.
150. Mulvaney-Day NE, Alegría M, Sribney W (2007) Social cohesion, social support, and health among Latinos in the United States. Soc Sci Med 64: 477-495.
151. Harvey IS, Alexander K (2012) Perceived social support and preventive health behavioral outcomes among older women. J Cross Cult Gerontol 27: 275-290.
152. Quinn TD, Wu F, Mody D, Bushover B, Mendez DD, et al. (2017) Associations Between Neighborhood Social Cohesion and Physical Activity in the United States, National Health Interview Survey, 2017. Prev Chronic Dis 16: E163.
153. Chia F, Huang WY, Huang H, Wu CE (2023) Promoting Healthy Behaviors in Older Adults to Optimize Health-Promoting Lifestyle: An Intervention Study. Int J Environ Res Public Health 20: 1628.