Health-adjusted life expectancy according to lifestyle classified by the Yonsei Lifestyle Profile-BREF

OBJECTIVES This study aimed to investigate health-adjusted life expectancy (HALE) by demographic characteristics (sex, educational achievement, and residential area) according to the lifestyle classifications of the Yonsei Lifestyle Profile-BREF (YLP-BREF). METHODS This study included 569 participants aged 55 years or older living in Korea. The YLP-BREF domains were physical activity, activity participation, and nutrition. RESULTS Females had a longer HALE (mean±standard deviation, 8.90±5.06 years) in the physical activity domain, while males had a longer HALE in the nutrition domain (9.44±5.91 years). People living in rural areas had longer HALE in physical activity (12.02±5.60 years), activity participation (8.58±4.21 years), and nutrition (11.33±6.43 years). There were no significant differences according to sex or residential area. High school graduates showed the longest HALE (physical activity: 10.38± 6.89; activity participation: 7.64±4.29; nutrition: 9.59±6.40 years). There was a significant difference in educational achievement. CONCLUSIONS As people age, the demand for a healthy lifestyle increases. This study attempted to calculate HALE by demographic characteristics according to lifestyle. The results of this study will help inform future research directions for providing a healthy lifestyle.


INTRODUCTION
used as an indicator of the quality of life, only the quantity [8,9]. Moreover, LE presents a critical weakness as it cannot reflect morbidity, disability, or health status [10]. To compensate for the disadvantages of LE [9,10], the concept of health-adjusted life expectancy (HALE) was developed in 1972 [11]. HALE is a summary measure that can estimate the average number of years that a person at a given age is expected to live in a completely healthy state [12]. The World Health Organization (WHO) used HALE as an official measurement method for annual reports to provide information on the average level of health of the population of member states [13]. Therefore, it is critical to consider not only the length of life, but also the HALE of older adults in order to increase the population's health and well-being.
A healthy lifestyle is considered to be an important factor for improving the HALE of older adults population [14]. The WHO stated that 60% of the factors affecting individuals' health are lifestyle-related characteristics. An unhealthy or unbalanced lifestyle causes various diseases, such as metabolic diseases, hypertension, joint and skeletal problems, and cardiovascular diseases [15,16]. Since lifestyle is associated with health and mortality, it is important to determine whether older people have healthier lifestyles.
Several lifestyle assessment tools exist, including FANTASTIC [17], the Health Enhancement Lifestyle Profile [18], and the Personal Lifestyle Questionnaire [19]. Although all these evaluation tools have been verified, there are some limitations to their application to older adults [20]. Specifically, existing evaluation tools do not focus on elderly individuals [21]. These measures also tend to focus on assessing the frequency and duration of specific lifestyle factors such as exercise, diet, smoking, and drinking [22][23][24][25][26]. Furthermore, most lifestyle evaluation tools were developed based on Western cultures. Therefore, these lifestyle assessments cannot measure the lifestyle of older adults in Asian cultures, as lifestyles tend to be shaped by the environment [27].
The Yonsei Lifestyle Profile-BREF (YLP-BREF) was developed to examine physical activity, activity participation, and nutrition and to identify individuals who engage in a healthy lifestyle [28]. The YLP-BREF includes 22 items related to 3 life elements: physical activity, activity participation, and nutrition. There are 5 physical activity questions, 4 activity participation questions, and 13 nutrition questions (a total of 22 questions). All 22 items are rated on a 5-point Likert scale. Participants respond by indicating the frequency with which they engage in a particular activity. Since the YLP-BREF has cut-off scores for each domain, it can classify individuals who have a healthy lifestyle.
Hence, it is worthwhile to analyze HALE by demographic characteristics according to lifestyle factors. Therefore, this study aimed to examine HALE by demographic characteristics (sex, educational achievement, and residential area) according to the lifestyle classifications of the YLP-BREF.
This study calculated HALE by dividing participants into those with a healthy lifestyle (1) and unhealthy lifestyle (0) using the YLP-BERF. After generating the classification of 0 or 1 using the cut-off score of YLP-BREF, the proportion of all participants with a score of 1 was calculated and applied as a weight when calculating HALE. In addition, HALE was divided into 3 variables, corresponding to different aspects of lifestyle, based on previous studies finding that there were differences in lifestyle according to demographic characteristics.

Data source and study design
The data used in this study were obtained from a study by Park et al. [29]. The subjects included in this dataset were 569 people 55 years of age or older residing in Korea between September and November 2020. The following were the criteria for collecting data: (1) people aged 55 years or older living in the local community and (2) people who have lived in Korea in recent years.

The Yonsei Lifestyle Profile-BREF
The original Yonsei Lifestyle Profile (YLP) is a 60-item questionnaire designed to measure the lifestyle of an individual in 3 domains-namely, physical activity, activity participation, and nutrition [30,31]. The YLP is composed of a 5-point Likert scale. The physical activity and activity participation domains consist of items on frequency, time, and satisfaction, and the nutrition domain consists of items on frequency and satisfaction. The YLP exhibited high internal reliability, with a Cronbach's alpha of 0.83. The test-retest reliability of YLP was 0.97 [31]. In contrast, the YLP-BREF consists of 22 items from the 60-item YLP [28]. The YLP-BREF consists of only frequency items. For example, physical activity and activity participation are assessed with items that have the following responses: (1) never, (2) 1-2 day/wk, (3) 3-4 day/wk, (4) 5-6 day/wk, and (5) 7 day/wk. The items on nutrition have the following responses: (1) never, (2) 1-2 day/wk, (3) 3-4 day/wk, (4) 5-6 day/wk, and (5) 7 day/wk. There are 5 frequency items in the physical activity domain, 4 in the activity participation domain, and 13 in the nutrition domain. A previous study reported that the cut-off scores for each domain of the YLP-BREF were 9 points for physical activity, 6 points for activity participation, and 28 points for nutrition [28]. In this study, using the cut-off scores of YLP-BREF, HALE was according to whether individuals had a healthy lifestyle (defined as a score of 1) or an unhealthy lifestyle (defined as a score of 0).

Defining health-adjusted life expectancy
The term "HALE" has recently been used to describe the same concept as disability-adjusted LE [32]. HALE is an estimate of the average number of years that a person at a given age is expected to live in a healthy state [12]. The "health" in HALE is selected using panel data or sample cohort's data on health-related quality of life or illness in a population. "Health" is usually expressed as a degree of disability [33]. The degree of disability is assessed using disability weights, which quantify the level of disability associated with a particular health condition or disease on a scale from 1 (completely healthy) to 0 (having a disability) [34]. Thus, com-not. The number of people with chronic diseases was 291 (51.14%), corresponding to more than half the total number of participants. Lastly, 304 (53.43%) of the participants lived in urban areas and 10 (1.76%) lived in rural areas.  pared to disease-free or disability-free LE, which evaluates the disability level as 0 or 1, HALE can more accurately account for disability levels. We calculated HALE using the cut-off score of YLP-BREF as the disability weight in the existing HALE calculation method. Therefore, in this study, disability weights were calculated based on a division into 0 (unhealthy lifestyle) and 1 (healthy lifestyle); specifically, the disability weight was the proportion of the total number of participants who had a healthy lifestyle. Since the YLP data used in this study were collected in 2020, the 2020 data from the life table related to mortality were utilized [35]. HALE was measured as follows: (1) Materials 1-3). The prevalence of healthy lifestyles was calculated as the proportion of participants with a certain value for an independent variable (demographic characteristics: sex, educational attainment, region) who had healthy lifestyles. (3) The number of years of survival by age was multiplied by the prevalence of each YLP domain to obtain the weight ( ) of the number of years of survival by age. (4) HALE ( ) was obtained by dividing the total number of years of survival ( ), which is the total number of years of survival at each age, by the number of survivors by age ( ). HALE can be measured using the following formulas:

Statistical analysis
A descriptive analysis was conducted to investigate demographic characteristics. The HALE according to the YLP-BREF was calculated using the Sullivan method. SAS version 9.4 (SAS Institute Inc., Cary, NC, USA) was used for data management and data analysis. We used sex, educational achievement, and residential area variables to calculate HALE for each demographic characteristic according to lifestyle.

Ethics statement
This study was approved by the Yonsei University Institutional Review Board (#1041849-202111-SB-202-01) and met the research exemption criteria of all participating institutions. Table 1 presents the demographic characteristics of participants who had complete YLP-BREF responses. This study included a total of 569 participants, with an average age of 60.23±4.26 years. Slightly more than half of the participants (n= 287, 50.44%) were males. Furthermore, 395 (69.42%) of the participants had a college degree, and 13 (2.28%) had an educational status of only middle school graduation or less. Regarding employment status, 296 (52.02%) of the participants were working, and 12 (2.11%) were (1)

RESULTS
HALE (10.38 ± 6.89 years). For the activity participation domain, middle school graduates or less had the shortest HALE (3.29 ± 1.53 years), whereas high school graduates had the longest HALE (7.64± 4.29 years). For the nutrition domain, middle school graduates or less had the shortest HALE (3.34 ± 1.51 years), whereas high school graduates had the longest HALE (9.59 ± 6.40 years).

DISCUSSION
This study aimed to determine whether there were differences in HALE between demographic characteristics according to lifestyle. In this study, healthy and unhealthy lifestyles were classified for each domain of the YLP-BREF. Using the weights of the classified lifestyles, HALE was calculated by demographic characteristics according to physical activity, activity participation, and nutrition. Differences in HALE appeared only according to educational achievement, and there was no significant difference according to sex or residential area.
HALE, according to sex, was longer in the physical activity domain for females and in the nutrition domain for males, but there was no significant difference. In a previous study, data from the Korea National Health and Nutrition Examination Survey were used to analyze differences in dietary habits and nutritional status between single-person and multi-person households. It was re-ported that, as age increased, the level of nutritional intake decreased in single-person female households more than in singleperson male households [36]. Furthermore, although the physique and physical function of older Korean females decreased with increasing age, no significant decrease in physical activity was observed [37]. In contrast, among males in their 20s to 60s, as their age increases, their muscle mass decreases, and as their body fat increases, their physical activity level also decreases [38]. Correspondingly, activity participation was also higher among females than males. Previous studies showed differences in physical activity and activity participation according to sex, but in our study, no significant differences were found between males and females. Despite the lack of statistical significance, the numerical difference in HALE showed the same direction as in the previous study.
The HALE by educational achievement according to each YLP-BREF domain was, on average, longest among high school graduates and shortest among middle school graduates or less, showing a significant difference. This indicates that physical activity decreases as age increases and educational attainment decreases [39,40]. As such, physical activity is related to educational achievement [41]. Accordingly, it can be observed that HALE according to the physical activity domain increases as educational achievement increases. In addition, Hall [42] reported that higher educational attainment increased the proportion of participation in social and leisure activities. In other words, as activity participation increases with higher levels of educational achievement, HALE in activity participation domain increases as the level of educational achievement becomes higher. Lastly, higher educational achievement was associated with a greater increase in HALE in the nutrition domain. Many studies have reported income inequalities according to educational attainment [43,44]. Higher educational attainment is associated with higher income and, accordingly, differences in nutritional intake. In addition, a study by Cembranel et al. [45] reported a higher intake of micronutrients among people with higher educational attainment than among those having lower educational attainment. Accordingly, higher educational achievement is associated with longer HALE in the nutrition domain.
In the area of physical activity, the HALE value was higher in rural areas than in urban areas, but without a significant difference. According to previous research, individuals living in urban areas are less physically active than individuals in rural areas [46,47]. Furthermore, individuals living in urban areas engage in less physical activity due to the convenience of available facilities, whereas individuals in rural areas engage in more physical activity due to the rural environment. According to a study by Lee et al. [48], rural residents reported more social and organizational participation, such as trust in neighbors, exchange of help with neighbors, and socializing with friends. Finally, in the nutritional domain, the HALE value was higher in rural areas than in urban areas, but again without a significant difference. Chung & Kim [49] reported that older adults living in urban areas consumed more meat, fish, milk, oily foods, ice cream, cakes, snacks, and carbonated drinks, whereas those in rural areas consumed more vegetables than urban dwellers. Accordingly, it has been reported that metabolic syndrome, the components of which include hyperlipidemia and hypertension, is highly likely to develop in older adults living in urban areas. Unlike previous studies, our study showed no significant difference in HALE values between residential areas. However, the direction of the HALE values showed similar results to those of previous studies. One of the reasons why no significant differences were found in our study may have been the impact of rural development. Kim [50] reported that the economic disparity between urban and rural areas was alleviated due to the revitalization of tourist destinations in rural areas. Reducing the financial gap leads to an increase in the quality of life in rural areas, which may explain the lack of a significant difference in this study, unlike previous studies. The purpose of this study was to calculate HALE according to demographic characteristics using the YLP-BREF. According to each calculated YLP-BREF domain, HALE varied according to demographic characteristics. However, significant differences were not found in a comparative statistical analysis of HALE according to sex and residential area (Supplementary Materials 4-6). As discussed above, previous studies have described reasons why lifestyles vary according to sex and residential area. The absence of statistically significant differences in HALE according to sex and residential area in our study seems to have been due to the biased data of 556 patients. Therefore, future studies should perform statistical comparisons by collecting various samples and applying the methodology of this study.
This study has several limitations. First, the data used were from people aged 55 years or older living in Korea. Therefore, the results cannot be generalized to adolescents and young adults. That is, the results may have been influenced by the age of the participants in the YLP-BREF survey. Second, since the YLP-BREF is a self-report questionnaire, it is affected by the tendency to provide socially desirable answers. Finally, this study used data from 569 participants that had already been collected. Despite using data from 569 participants, the number of participants with middle school education or less was relatively small. The LE calculated by educational attainment for middle school graduates or less was based on a few people, and the results might therefore not be generalizable. Therefore, it is necessary to analyze data from participants with various levels of educational attainment in future research.
Our study calculated HALE according to demographic characteristics and lifestyle. For each domain of the YLP-BREF, no significant differences were found in HALE by sex or region, whereas there was a significant difference in HALE by education. The results of this study may help in research on interventions for healthy lifestyles and related health programs in the future. However, since data from a small sample were used in this study, it is necessary to calculate HALE according to lifestyle data from a broader variety of older adults in future studies.