INTRODUCTION
1. Background
Successful aging constitutes an important aspect of public health [1]. As age extends, physical and medical problems occur. Moreover, complex and diverse problems in society and families are associated with aging, such as parental support issues, social costs, retirement security programs, and difficulty accessing information and communication networks [2]. To overcome these problems that threaten a happy and leisurely life in old age, it has become more critical that the correct awareness and action plan for successful aging. Additionally, as the life span of the older adults increases, the prevalence of chronic diseases is also rising [1], making successful aging among older adults increasingly important.
Based on the activity theory, many successful aging theorists have defined successful aging as avoiding diseases and disabilities, maintaining a high level of physical and cognitive function, and actively participating in life through interpersonal relationships and productive activities [3]. However, this definition focused on prolonging the middle-aged/older generations’ advantages as much as possible rather than conceding the unique trait of aging. Further, it did not consider that it could be an evaluation index for the subjective aspects of aging, such as, life satisfaction, quality, and purpose of successful aging caused by overly emphasizing function and productivity [4].
Theorists in developmental psychology, particularly in the study of aging, presented a Selection-Optimization-Compensation (SOC) strategy for successful aging based on interindividual variability and intraindividual plasticity [5]. The SOC strategy has been described as the process of effectively adapting and developing despite the loss of normal functions due to aging. Thus, even though the older adults experience loss and decreases in function and capacity due to aging, it is referred to as an adaptation process that allows the older adults to select possible activities depending on their abilities, to optimize by maximizing the remaining resources, and to compensate for reduced function, health, and disability. Therefore, the SOC strategy compensates for existing limitations of the successful aging theory and could have high effectiveness as a strategy for successful aging in older adults with different health levels [6].
It is necessary to help degenerative osteoarthritis (DA) older adults whose quality of life has been reduced for a long time to find a good life even in this situation. It is predicted that the successful aging of older adults with DA can play many important roles in social and economic aspects as aging progresses.
Successful aging in large population is one of the main research goals of public health in aging societies world wise. Therefore, discussing successful aging without considering older adults with chronic diseases is not in line with public health. Moreover, specific studies on successful aging in the increasing number of older adults with chronic diseases are needed. Among chronic diseases, especially older adults with DA have pain and joint stiffness as its main symptoms, limiting the activities and making them lethargic. Therefore, research is needed so that these older adults can achieve successful aging and enjoy a high quality of life. However, successful aging research on older adults with DA is rare. Therefore, we conducted research using the SOC strategy targeting older adults with functional decline due to DA, and based on the results, we aim to provide basic data for intervention strategies so that more older adults with DA can achieve successful aging.
2. Conceptual Framework and Hypothetical Model
The SOC strategy was developed to establish an effective successful aging strategy that helps individuals adapt well without losing self-efficacy, even when certain functions are lost and general reserve capacity declines due to aging [5]. Selection refers to a strategy of selecting and narrowing areas with high priority and identifying what individuals can do. Even if an individual’s performance range is reduced through this selection strategy, a ‘reduced and transformed but effective life’ could be enjoyed even in old age [7]. Optimization is an effort to maximize an individual’s selection in quantitative and qualitative aspects [8]. Compensation is to redeem for the losses by learning, equipment aids, external help, and psychologic compensatory mechanisms when loss of biologic, social, and cognitive functions occurs [8].
The dynamic process of the SOC model was developed based on the premise that the three components of the SOC strategy are not mutually exclusive but are interconnected [5]. The model consists of antecedent condition, process, and outcome. The antecedent condition means limited resources and losses of internal and external resources with aging. The process of using the SOC strategy leads to the outcome. By maximizing gains and minimizing losses, older adults can live an effective life [9]. The essence of the dynamic process is the ‘process of adaptation’.
This study investigated internal aspects (physical and emotional factors in patients) and external (social support factors) as an antecedent condition that could affect the successful aging of older adults with DA. As physical factors, the study evaluated pain and subjective health status. Pain is a major symptom in patients with DA, and when severe, it can lead to depression and reduced social activities [10]. Since pain can change daily life and overall quality of life, it was considered a factor to be identified to achieve successful aging. Subjective health status refers to the older adults evaluating their health based on their self-defined health criteria, that is, how they perceive their health [11]. In a systematic literature review, the higher the subjective health status, the higher was the successful aging status [12].
Depression, as an emotional factor, can influence successful aging [12,13]. A study in older adults found that depression greatly affected the SOC strategy and successful aging [14]. Older adults with DA experienced more depression than healthy or older adults without DA because of the symptoms of DA and subsequent limitations of daily life [15].
Social support factors included healthcare provider support and family support. A meta-analysis of successful aging factors also revealed that family support strongly influences [16]. Older adults with DA, a chronic disease, need to continue medical treatment. Medical support provided by medical professionals positively affects patients and enables active motivation for health-promoting behavior and rehabilitation [17]. Therefore, it attempted to understand the effects of social support on successful aging and SOC strategy in older adults with DA.
The hypothetical model of this study consisted of four exogenous variables and two endogenous variables (Figure 1). The exogenous variables were physical factors, depression, healthcare provider support, and family support. The endogenous variables were SOC strategy and successful aging, and a hypothetical model was established through a literature review.
Physical factors, depression, healthcare provider support, and family support were set as directly influencing successful aging. It was also assumed that the SOC strategy directly affected successful aging and that physical factors, depression, healthcare provider support, and family support directly influenced the SOC strategy. Moreover, the hypothetical model of nine path was constructed assuming that physical factors, depression, healthcare provider support, and family support could indirectly affect successful aging through the SOC strategy.
METHODS
Ethic statement: This study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board (IRB) of the Kongju National University (No. KNU_IRB_2019-80). Participants voluntarily signed a consent form to participate in the survey.
1. Study Design
This study aimed to build a structural model for successful aging in older adults with DA using a SOC strategy, test the goodness-of-fit between the structural model and actual data, and evaluate the direct and indirect effects of various influencing factors. There were 11 measurement variables: physical factors (pain, subjective health status), depression, healthcare provider support, and family support were configured as exogenous variables, and SOC strategy and successful aging were set as endogenous variables. This study was performed according to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Reporting Guidelines (https://www.strobe-statement.org/).
2. Participants and Data Collection
This study included patients aged 65 years or older living in Nonsan city and Daejeon metropolitan city in Chungcheongnam-do and who had been diagnosed with DA by a specialist for more than 6 months. Data was collected using structured questionnaires, from December 2019 to February 2020, from a total of 220 people (155 females, 65 males). Older adults with stroke, central nervous system disorders, diagnosed dementia, or those with poor communication skills were excluded from the study. The researcher and three pre-trained assistants provided help if the participants had questions or had difficulty completing the questionnaire.
The minimum required sample size for testing the model using the maximum likelihood estimation method is 200 or more [18]. Therefore, considering a 10% dropout rate, 230 questionnaires were distributed for this study. We excluding 10 questionnaires that were not completed, marked repeatedly, or dropped out. A total of 220 questionnaires were used for the final data analysis.
3. Measurement
1) Physical Factors
(2) Subjective health status
The Health Self-Rating Scale developed by Northern Illinois University, translated into Korean by Kim and Park [19], was modified to three items and used in this study. A 5-point Likert scale ranges from 1 point of ‘very bad’ to 5 points of ‘very good’, higher scores indicate better subjective health. Cronbach’s α indicating tool reliability was .79 in Kim and Park [19] and .81 in this study.
2) Depression
Depression was measured using the Geriatric Depression Scale Short Form-Korea version of Kee [20], which was modified and supplemented by the Geriatric Depression Scale Short Form developed by Sheikh and Yesavage [21] to suit the Korean situation. With a total of 15 items, ‘yes’ is 1 point, ‘no’ is 0 points, and higher scores indicate more severe depression. Negative items were calculated by reverse conversion. Cronbach’s α of the tool’s reliability was .85 at development [21] and .88 in this study.
3) Healthcare Provider Support
Healthcare provider support was assessed using the 8-item medical support measurement tool developed by Tae et al. [22], which was modified to six items in this study to suit older adults with DA. The six items consisted of: “Does listening to my complaints without criticism?”, “Praise for regular hospital visits and treatment”, “Explaining and participating in treatment”, “Feedback on regular hospital visits”, “Encouragement not to be disappointed with treatment”, and “Ventilation with positive words when patients are in distress”. Each item consisted of a 5-point Likert scale with 1 point for “not at all” and 5 points for “always did it.” a higher score indicated a higher level of healthcare provider support. Cronbach’s α was .84 in development [22] and .86 in this study.
4) Family Support
Family support was assessed using a tool that was modified and supplemented based on the family support measurement tool developed by Tae et al. [22] to suit older adults with DA in this study. The measurement tool comprised eight items in total and consisted of a 5-point Likert scale with 1 point for “not at all,” and 5 points for “always did it.” higher scores indicate higher perceived family support. Cronbach’s α was .82 in development [22] and .93 in this study.
5) Selection-Optimization-Compensation Strategy
The SOC strategy was measured using the tool developed by Baltes et al. [23] and modified by Oh [14]. The questionnaire comprised six items on selections, six on optimizations, and six on compensations related to DA. Each item had two options: i.e., relating to high or low use of the SOC strategy? The total score ranged from 0 to 18, with a higher score indicating a higher level of SOC strategy use. Cronbach’s α was .81 in Baltes et al. [23], .76 in Oh [14], and .72 in this study.
6) Successful Aging
Successful aging was measured in three aspects: physical, psychologic, and social. The overall Cronbach’s α was .83 in this study. The degree of physical successful aging of the study participants was measured using the three items developed by Kim [24] and modified in the study of Seo [25]. The tool was a Likert-type scale with 1 point for ‘not at all’ and 5 points for ‘strongly agree’, a higher score indicated more successful aging regarding physical aspects. Cronbach’s α was .84 in Kim’s tool [24], .83 in Seo’s study [25], and .72 in this study. Regarding psychologic successful aging, six psychologic items in the Satisfaction with Life Scale developed by Diener et al. [26] were measured with a three items tool modified by Oh [14]. The latter tool is a Likert-type scale measuring 1 point for ‘not at all’ and 5 points for ‘strongly agree’. Higher scores indicate more successful psychologic aging. Cronbach’s α was .87 in the original study [26], .80 in Oh [14], and .84 in this study. Social successful aging was measured with a tool developed by Kim and Sin [27] and modified by Seo [25] with five items: the tool comprises a Likert scale of 1 point for ‘not at all’ and 5 points for ‘strongly agree’, with higher scores indicating more successful social aging. Cronbach’s α was .94 in the original study [27], .78 in the study of Seo [25], and .84 in this study.
4. Data Analysis
The data were analyzed using SPSS ver. 25.0 (IBM Corp.) and AMOS ver. 20 (IBM Corp.). Frequencies, means, and standard deviations (SDs) for patients’ demographic and baseline characteristics were analyzed using descriptive statistics. The reliability analysis of the tool was calculated using Cronbach’s α. To test normality of the sample, skewness and kurtosis were calculated, Multicollinearity between variables was analyzed using tolerance and variation inflation factor (VIF), and the correlation between research tools was analyzed using the Pearson correlation coefficient. The goodness-of-fit was assessed using both absolute and incremental fit indices, including the chi-square statistic χ2 (CMIN: recommended level p>.05), χ2/df (CMIN/DF: ≤3), the goodness-of-fit index (GFI: ≥0.90), the adjusted goodness-of-fit index (AGFI: ≥0.90), the standardized root mean square residual (SRMR: ≤0.05), the root mean square error of approximation (RMSEA: ≤0.10), the normed fit index (NFI: ≥0.90), and the comparative fit index (CFI: ≥0.90) [18].
RESULTS
1. General Characteristics
The total number of subjects was 220 and mean±SD age was 74.9±6.76 years old, with 102 (46.4%) aged 71 to 80 occupying the highest proportion, 155 female older adults (70.5%) and 65 male older adults (29.5%). A total of 160 people (72.7%) were married, 58 people (26.4%) reported spouse bereavement, and 2 (0.9%) were divorced. As for the type of cohabitation, the number of older adults living with a family was 156 (70.9%), while the number living alone was 64 (29.1%). The most common lesion diagnosed with DA was the knees in 120 patients (54.5%). Furthermore, regarding questions other than DA, the presence of other diseases was 'yes' for 163 (74.1%) and 'no' for 57 (25.9%). Regarding the treatment of DA, 199 patients (90.5%) answered “yes” and 21 patients (9.5%) responded “no”.
2. Correlation Between Descriptive Statistics and Measurement Variables in a Hypothetical Model
The descriptive statistical results of the study variables are shown in Table 1. And correlation between variables is shown in Table 2. In this study, the absolute values of skewness and kurtosis of the measured variables were less than 2, respectively, which satisfies univariate normality. So, structural model analysis was performed.
Generally, when testing a hypothetical model, if the analysis of the correlations among the measured variables used in the model shows a high multicollinearity, which indicates a strong correlation among the measured variables, the variables explained decrease. In this study, the correlation coefficient between the measured variables was 0.7 or less. In the tolerance and VIF diagnosis, the tolerance limit values were all 0.1 or more, and all VIFs did not exceed 10. Therefore, it was no problem with multicollinearity between the measured variables.
3. Hypothetical Model Testing
Confirmatory factor analysis was performed to evaluate the hypothetical model’s validity. The absolute value of the critical ratio (CR) of 1.96 or more (p<.05) of ‘non-standardized λ’ [18], which is the minimum condition required for validation, was satisfied with the lowest value of 4.38 in this study. When looking at the convergent validity, it can be confirmed that the correlation in physical aging, an observational variable of successful aging, was low. It also indicated that the factor load was less than 0.5, which affected the standardized value [18]. Therefore, physical aging, an observed variable that does not explain successful aging well, was removed and analyzed in the structural model. To evaluate the validity of the measurement tool, single observed variables (depression, healthcare provider support, and family support) were allocated as (1-α)*variance at the first term and α*SD at the pathway. After that, average variance extracted (AVE) and construct reliability values were checked. In this study, AVE 0.5 or higher of the construct [18], construct reliability convergent validity was confirmed with a value of 0.7 or higher. It also indicated discriminant validity since the square values of the correlation coefficients of the latent variables are all larger than the AVE values. As a result of checking the GFI of the hypothetical model, were all acceptable levels as follows: χ2 (CMIN)=56.76 (p<.001), CMIN/DF=2.46, GFI=0.95, AGFI=0.88, CFI=0.94, NFI=0.91, SRMR=0.05, and RMSEA=0.08. The variables explained in the model were 25.6% for SOC strategy and 85.3% for successful aging.
To identify the overall effectiveness between latent variables, standardized direct effect, indirect effect, and total effect were analyzed using the bootstrapping method. Factors affecting the SOC strategy were depression and healthcare provider support. Depression had a direct effect (β=-.34, p=.013), and healthcare provider support had a direct effect (β=.13, p=.047), which showed a statistically significant effect. Factors influencing successful aging were depression, healthcare provider support, family support, and SOC strategy. For depression, it showed statistically significant that the direct effect (β=-.67, p=.007), the indirect effect (β=-.07, p=.005), and the total effect (β=-.74, p=.008). In the case of healthcare provider support, the direct effect (β=.20, p=.012), the indirect effect (β=.02, p=.036), and the total effect (β=.23, p=.012) were statistically significant. In addition, the direct effect (β=.14, p=.028) of family support was statistically significant, and the direct effect (β=.21, p=.007) of the SOC strategy was found to have a statistically significant effect.
4. Modifying the Hypothetical Model
As a result of analyzing the hypothetical model, the GFI of the model reached the standard. Still, the model was modified to increase the simplicity and goodness-of-fit of the measurement model. In this study, no modification rule met items existed when the modification index (MI) was used as a standard. Therefore, it used a method to find a model with a high goodness-of-fit while deleting the path from the existing research model without using the MI. The method was modified by removing the track from physical factors with low estimate value and insignificant pathways to successful aging and from family support to SOC strategy one by one or deleting both pathways together. Finally, a total of nine paths were changed to six modified routes, and a simple modified model was defined as this study’s final modified model, including increasing the goodness-of-fit (Figure 2).
5. Testing and Analysis of Modified Models
The goodness-of-fit test result of the modified model are χ2=53.44 (p<.001), CMIN/df=2.13, GFI=0.96, AGFI=0.90, CFI=0.95, NFI=0.92, SRMR=0.05, RMSEA=0.07, which was improved compared to the hypothetical model. The results of analysis of standard error, CR, the direct, indirect, and total effects of the modified model are shown in (Table 3). The variables explained of the modified model were 25.7% for the SOC strategy and 86.2% for the successful aging. Moreover, the variables influencing the SOC strategy were depression (β=-.35, p=.002) and healthcare provider support (β=.14, p=.048). It was found that the lower depression, the better the healthcare provider support, and the positive effect on the SOC strategy. Variables influencing successful aging were depression (β=-.70, p=.002), healthcare provider support (β=.20, p=.002), family support (β=.14, p=.041), and the SOC strategy (β=.21, p=.020). The lower the depression, the higher the healthcare provider support and family support, and the better using the SOC strategy, it was indicated more positive effects on successful aging. The SOC strategy had a statistically significant direct effect on successful aging. For depression, the direct effect (β=-.70, p=.002), the indirect effect (β=-.07, p=.001), and the total effect (β=-.77, p=.006) were statistically significant. For healthcare provider support, it was noted that the direct effect (β=.20, p=.002), the indirect effect (β=.02, p=.044), and the total effect (β=.23, p=.022) were statistically significant.
DISCUSSION
This study constructed and tested a hypothetical model based on the SOC strategy to identify the factors influencing successful aging in older adults with DA. The hypothetical model was constructed with physical factors (sub-variables: pain, subjective health status), depression, healthcare provider support, and family support as exogenous variables, and SOC strategy as an endogenous variable. Then, the goodness-of-fit of the model was tested, and the direct, indirect, total effects of the variables were analyzed.
The modified model used in this study was suitable as most of the goodness-of-fit satisfied the recommended levels. In the final modified model, depression, healthcare provider support, family support, and SOC strategy explained successful aging of the older adults with DA in 86.2% and depression and healthcare provider support explained 25.7% of SOC strategy.
In a successful aging study using the SOC strategy for older adults with chronic obstructive pulmonary disease (COPD) [28], social support (including healthcare provider support and family support), coping strategy, dyspnea, and SOC strategy affected successful aging. Moreover, at this time, the variables explained were 62.9%, and the SOC strategy was explained 39.3% by social support and coping strategy. In a study on the local older adults [14], the functional health status and SOC strategy on successful aging affected 86%, and depression explained 50% of successful aging through SOC strategy as a medium. Compared with previous studies, our study showed relatively high variables explained for the direct effect on successful aging, and the SOC strategy indicated relatively low variables explained. However, since each study applied different variables, comparing horizontally with only the variables explained is unreasonable. Therefore, it is necessary for a follow-up study considering the development of new variables that can higher the variables explained in the SOC strategy, which showed relatively low variables explained in this study.
Depression was a direct influencing factor on the SOC strategy and was also identified as an indirect influencing factor in successful aging. Therefore, the lower the incidence of depression, the higher the SOC strategy level and successful aging level. Oh [14] attributed these results to characteristics of the SOC strategy that explain adaptation to life changes through psychologic compensatory mechanisms. Young et al. [29] also argued that successful aging is possible even in older adults with disease by psychological factors acting as a compensatory action. This study is similar in that it attempted to achieve successful aging through emotional and social compensation for depression, healthcare provider support, and family support rather than through an influence on physical factors such as pain and subjective health status. Depression is one of the major influencing factors of successful aging in older adults with DA. Therefore, in situations where psychologic function declines, such as depression in old age, the SOC strategy, allows adaptation and compensation, and is helpful for successful aging. In addition, there is an urgent need to develop, test, and implement an active and practical intervention strategy to reduce depression in older adults with DA. Further, depression showed a marked correlation with physical factors. To reduce depression, it is necessary to control pain, maintain activities of daily living and intervene to positively influence subjective health status.
As a result of this study, healthcare provider support showed a direct effect on successful aging and a significant indirect effect through the SOC strategy. In a study on older adults with COPD, one of the chronic diseases, social support, including healthcare provider support, showed direct and indirect effects on successful aging using the SOC strategy [28]. Higher medical staff support was associated with higher self-care activities [30], and healthcare provider support offers psychological stability and overcoming frustration [31]. For older adults with DA, healthcare providers direct treatment, education, and rehabilitation, so they positively affect the decrease and loss of physical function, thereby encouraging efforts and active motivation to strengthen remaining ability without being frustrated. It is believed that such support from healthcare providers may increase the successful aging level by further enhancing the SOC strategy. Specifically, healthcare providers should listen carefully to patients’ complaints and plead, provide information, and educate them. Involving the patient in establishing a treatment plan and providing continuous positive encouragement can be an important support.
Physical factors such as pain and subjective health status showed no significant direct or indirect effects on SOC strategy and successful aging. In particular, it is an interesting result that pain did not affect successful aging. These results are thought to be because the study was conducted by limiting the subjects to chronic DA patients. Since pain is the most common symptom of DA and the study was conducted in chronic older adult patients, almost all patients complained of pain, the level was also high with an average of more than 6 points. It is thought that the high level of pain in all subjects makes the subjects lethargic, and the response to the pain itself has also been reduced to reduce the activity or simplified with medication so that efforts for successful aging have been avoided. In correlation analysis, the greater the pain, the less was the use of selection and optimization strategies. In the study of DA and a SOC strategy [32], a wide range of compensations were possible to deal with losses due to OA and compensations were expected to be reported more often when older adults experienced pain, had had surgery, or reported disability with necessary activities of daily living [33]. In the study of 77 years older adults, compensation was particularly important for individuals with low physical function [6]. These results indicate that patients with chronic DA focus more on compensation than on efforts to improve their condition as they age and experience more pain. Therefore, successful aging in older adults with chronic DA should actively consider emotional and social support for patients with problems such as depression, and healthcare provider support, rather than pain management.
The SOC strategy showed a direct effect on successful aging. That is, increased use of the SOC strategy that selects and optimize despite a reduction in general reserve capacity and compensate for the loss of functions, the greater the level of successful aging.
An intervention program that can strengthen the SOC strategy is necessary, and it is required for development depending on the three given strategies.
First, selection can be considered to help people choose high-priority actions and efforts in the face of lost resources due to aging and DA. Second, optimization should provide advice, encouragement, and support by intervening in the process of adapting, striving, and developing the chosen behavior and environment. In addition, it may be considered to strengthen the home and nursing care programs in preparation for a decrease in medical facility use due to decreased activity. Third, compensation can offer really physical and mental rewards for the loss. For example, assistive devices such as walkers and canes can be actively recommended and educated. It should also include developing and implementing a sustainable rehabilitation exercise program that can improve joint performance.
Through the above discussion, for the successful aging of the older adults with DA, it is necessary to reduce depression, which has shown a significant effect, and to improve healthcare provider support, family support, and SOC strategy. Especially, it is necessary to find ways to strengthen depression intervention programs and healthcare provider support. In addition, it is necessary to develop a SOC strategy reinforcement program that reflects the characteristics of the three elements in the SOC strategy for older adults with DA and to help older adults use them in life.
CONCLUSION
To increase the goodness-of-fit of the hypothetical model, a modified model in which six out of nine paths were finally supported was established by deleting existing paths with low estimates and insignificant paths. In the modified structural model of this study, depression, and healthcare provider support were found to be variables affecting successful aging using the SOC strategy. Therefore, for the successful aging of older adults with DA, it is important to actively intervene in depression and promote support for healthcare provider support. It is necessary to intervene for successful aging by actively utilizing the SOC strategy despite pain and loss of function.
The subjects of this study were 155 older females (70.5%) and 65 older males (29.5%), with a small proportion of males. Therefore, it may be some limitations in discussing the successful aging of older adults with DA regardless of sex. Consequently, it is necessary to conduct research by statistically matching the sex ratio.
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