A Study on the Development of a Fitness Age Prediction Model: The National Fitness Award Cohort Study 2017-2021

doi:10.21203/rs.3.rs-3719192/v1

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Research Article

A Study on the Development of a Fitness Age Prediction Model: The National Fitness Award Cohort Study 2017-2021

https://doi.org/10.21203/rs.3.rs-3719192/v1

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published 27 Sep, 2024

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Background

Physical fitness is considered an important indicator of the health of the general public, and in particular, the physical fitness of the elderly people is an important criterion for determining the possibility of independent living. Therefore, the purpose of this study was to examine the association between chronological age and physical fitness variables in the National Fitness Award Cohort study data and to develop multiple linear regression analyses to predict fitness age using dependent variables.

Methods

Data from 501,774 (359,303 adults, 142,471 elderly) individuals who participated in the Korea National Fitness Award Cohort Study from 2017 to 2021 were collected. The physical fitness tests consisted of 5 candidate markers for adults and 6 candidate markers for the elderly adults to measure muscle strength, muscle endurance, cardiopulmonary endurance, flexibility, balance, and agility. Pearson’s correlation and stepwise regression analyses were used to analyze the data.

Results

We obtained a predicted individual fitness age values from physical fitness indicators for adults and elderly individuals, and the mean explanatory power of the fitness age for adults was [100.882 – (.029 × VO2max) – (1.171 × Relative HGS) – (.032 × Sit-up) + (.769 × Gender Male = 1; Female = 2) + (.769 × Gender = 2)] was 93.6% (adjusted R2); additionally, the fitness age for elderly individuals was [79.807 – (.017 × 2-minute step test) – (.203 × 30-second chair stand) – (.031 × 30-second chair stand) – (.052 × TUG) + (.985 × TUG) – (3.468 × Gender Male = 1; Female = 2)] was 24.3% (adjusted R2).

Conclusions

We suggest the use of fitness age as a valid indicator of fitness in adults and the elderly individuals as well as a useful motivational tool for undertaking exercise prescription programs along with exercise recommendations at the national level.

Fitness age

Aging

Fitness assessment

Health outcomes

Due to the increase in life expectancy, the era of the 100-year-old generation is expected to occur at a rapid pace [1, 2]. Accordingly, in an aging society due to an increase in life expectancy, the burden of medical expenses is increasing due to a decrease in physical activity and an increase in lifestyle diseases such as obesity due to excessive nutrition [1, 3, 4]. According to previous studies, chronic diseases such as high blood pressure, diabetes, and high cholesterol are generally increasing due to the increased prevalence of obesity in adults over the age of 30 [5–7]; and 9 out of 10 elderly people live with more than 1 chronic disease and more than half of the elderly people have more than 3 overlapping diseases [8]. However, despite the spread of chronic diseases, efforts to prevent them are insufficient, and people's dietary habits have deteriorated, such as an increase in high-calorie, high-fat-oriented diets, and irregular meals, have deteriorated while aerobic physical activity and resistance exercise practices continue to decrease [9, 10].

The physical fitness level is considered an important indicator of the health of the general public [11–14], and in particular, the physical fitness of the elderly people is an important criterion for determining the possibility of independent living [15, 16]. Physical fitness includes factors related to health conditions such as musculoskeletal and cardiorespiratory fitness [14, 17]. Typical physical fitness tests include muscle strength (hand grip strength tests) [11, 12, 18], muscular endurance (30-s chair stand test and sit-ups test) [11, 18, 19], flexibility (chair sit-and-reach test) [18, 20], balance (time up-and-go) [12], and cardiopulmonary endurance (graded exercise test and 2-minute step test) [21–23]. The association between physical fitness and health status has been reported in several studies [13, 23–25]. Therefore, everyone is experiencing the same physical aging process differently due to lifestyle, the environment, and the influence of epigenetics, which is why many studies have focused on biological age [24, 26, 27]. Biological age gradually decreases in viability over time and increases with age, which ultimately leads to disability and mortality [26]. Thus, physical fitness age is increasingly needed in aging studies to measure the progression of biological aging processes, rather than simply measuring the passage of time. Several studies have been conducted and reported due to the importance of physical fitness age [2, 12, 18], but studies that have been reported thus far show that there are statistical limitations in standardizing the results analysis, as studies on one gender or studies with limited age and small sample sizes. Although there are only a few studies on this topic, research on the development of estimation formulas for physical fitness indicators also has complex measurement variables and is far from complete [28, 29]; moreover systematic large-scale reports on fitness age estimation formulas through scientifically rigorous large-scale national studies, which collect more than 100,000 data each year for Koreans, are limited.

Therefore, the purpose of this study was to examine the association between chronological age and physical fitness variables in the National Fitness Award Cohort study data and to develop multiple linear regression analyses to predict fitness age using dependent variables (e.g., muscle strength, muscular endurance, cardiopulmonary endurance, flexibility, balance). To the best of our knowledge, this study is the first large-scale study reported in Korea and is expected to present major trends and standardizations in future health-related fitness age analyses. Furthermore, this study is expected to serve as the foundation for customized exercise program services and for frailty classification via fitness variable analysis.

Data Sets and Study Participants

A total of 501,774 (male = 236,852 and female = 264,922) participants in the National Physical Fitness 100 Elderly Health Promotion Class exercise program at 82 sites nationwide (76 sites, 6 mobile sites) in South Korea from 2017 to 2021. The criteria for selecting subjects were those who voluntarily agreed to the purpose and contents of the study and those who could not measure their physical strength due to acute cardiovascular disease, systemic infections, or muscle and skeletal injuries were excluded. The physical characteristics of the study subjects are shown in < Table 1>. Ethical approval of this retrospective study was given by the Institutional Review Board (IRB) of the Seoul National University Boramae Medical Center, and all subjects provided their written informed (IRB No. 07-2024-2).

Body composition

Body composition was measured using multifrequency impedance, and all metallic materials were removed, shoes and socks were removed, both feet were placed on the electrodes of the tread plate while wearing light clothing, and the electrodes of both handles were held correctly and the arms were spread out approximately 30°. The results were measured once, and the figures on the results were recorded. Body mass index (BMI) was calculated using the ratio of weight squared to height (kg/m²). The height was measured in units of 0.1 cm after the study participants did not tilt their heads sideways in a natural upright position with their backs to the renal system and standing barefoot. The waist circumference was relaxed and straightened as much as possible and then measured horizontally, and the navel area was measured [2, 30].

Muscle strength (Grip strength test)

Handgrip strength was measured, and in the upright position, the grip of the grip meter (GRIP-D 5101; TAKEI, Co., Japan) was held at the second joint of the finger. The arm was straightened and the body and arm were pulled at 15 degrees for 5 seconds. The highest value was recorded in units of 0.1 kg by conducting two times on the left and right sides. Relative handgrip strength was defined as the value obtained by dividing the absolute handgrip strength by the body mass index [11, 30].

Muscular endurance (30-s chair stand test and sit-ups)

Muscular endurance was measured by sitting on a chair and standing up and sip-ups. The participants sat in the center of the chair, with the soles of both feet touching the ground, and the arms crossing in front of the chest. The participants sat on the chair for 30 seconds with the start chant and repeatedly stood up. If the study participant stood up halfway through at the end of 30 seconds, this was considered the number of times he stood up. The sit-up was performed by raising both elbows to the thigh after lying on the back of the mat with the knees bent approximately 30 cm away from the hip, and raising the upper body after the signal started, and the measurement unit was recorded as the number of measurements was recorded for 1 minute [11].

Cardiopulmonary Endurance (2-min Step Test and Estimated VO_2max)

Cardiopulmonary endurance was measured by walking in place for two minutes and estimating VO₂max. After and after adjusting the height by attaching a rubber band to both pillars of the support at the same height as the point marked on the thigh, the study participants raised the knee from the right foot with the start signal. When both feet were fully walked, the number of participants was counted once, and the number of repetitions for a total of 2 minutes was recorded. A graded exercise treadmill test using the Bruce protocol was applied to measure VO₂max. All participants started walking at a speed of 2.7 km/h with a slope of 10%. The speed was increased by 1.3–1.4 km/h at 3-minute intervals, and the incline was increased by 2% during each stage. graded exercise tests were performed on treadmills (TM55 treadmills; Quinton Cardiology Systems, Inc., Seattle, WA, USA). Heart rate was measured using a heart rate monitor (Quinton Q-Stress, Quinton Cardiology Systems, Inc., Bothell, WA, United States). Participants were expected to reach one of three of the following criteria: (1) a heart rate reserve rate > 85%, (2) a heart rate that did not increase with increasing steps, (3) a rating of perceived exercise > 17 (range: 6–20), and (4) no stop request [21, 22].

Flexibility (chair sit-and-reach)

Flexibility was measured by sitting and bending in front of the upper body. The subject took off his shoes and sat upright with his knees stretched so that the soles of both feet completely touched the vertical surface of the measuring instrument. Both hands were stretched to prevent the palms from touching the measuring instrument and bending the knees, and the upper body was bent to extend as far forward as possible. The maximum temperature was measured twice and recorded the maximum value was recorded in units of 0.1 cm [18, 20].

Balance (time up-and-go)

Agility was measured by sitting on a chair and returning to a 3 m target. When the participants were sitting in the chair, the time needed to return to the chair at 3 m from the chair with a signal and return to the chair again as fast as possible was measured. At this time, when the start signals sounded, measurements were made regardless of whether the study participants started moving. Measurements were made twice and the maximum value was recorded at 0.001 sec [12].

Statistical analyses

Statistical analyses were performed using the Statistical Package for the Social Sciences (SPSS) version 25.0 (IBM Corporation, Armonk, NY, US). The mean and standard deviation were calculated for all the measured parameters. Descriptive statistical analysis, Pearson’s correlation analysis, and stepwise regression analysis were also conducted. Pearson’s correlation analysis was used to analyze the relationships between age and physical fitness parameters in the adult and elderly groups. Stepwise regression analysis was applied to develop of the prediction equations according to age group. To perform multiple linear regression analysis, the β-value (the regression coefficient) was used to verify whether the independent variables had explanatory power [31]. In this work, we used the stepwise mode of regression analysis, which is indicated when multiple independent variables are taken as predictors. The threshold for statistical significance was considered with a p-value < 0.05.

Table 1

Baseline characteristics of subjects
	Adults			Elderly
	Male n = 182,972	Female n = 176,331	Total n = 359,303	Male n = 53,880	Female n = 88,591	Total n = 142,471
Age, yrs	35.58 ± 13.82	42.66 ± 14.49	39.06 ± 14.59	73.26 ± 5.28	72.41 ± 5.51	72.73 ± 5.44
Height, cm	173.05 ± 6.19	159.26 ± 5.83	166.28 ± 9.15	165.24 ± 5.86	152.48 ± 5.49	157.29 ± 8.36
Body weight, kg	74.44 ± 10.87	58.25 ± 8.62	66.49 ± 12.74	66.69 ± 8.88	57.62 ± 7.96	61.04 ± 9.41
Body fat, %	21.79 ± 6.67	31.05 ± 6.80	26.33 ± 8.17	26.38 ± 6.71	35.16 ± 14.43	31.85 ± 12.84
BMI, kg/m²	24.75 ± 2.97	22.92 ± 3.19	23.85 ± 3.21	24.45 ± 10.13	24.82 ± 9.17	24.68 ± 9.54
Waist cir, cm	85.45 ± 9.10	79.24 ± 8.67	82.10 ± 9.40	86.07 ± 8.45	83.08 ± 8.30	84.24 ± 8.48
Grip Strength, kg	34.35 ± 10.56	30.50 ± 10.38	32.46 ± 10.65	31.19 ± 6.57	19.65 ± 4.79	24.00 ± 7.87
Relative HGS, %	54.91 ± 11.79	49.60 ± 12.04	52.31 ± 12.20	50.67 ± 9.64	37.58 ± 8.51	42.52 ± 10.97
Sit and reach, cm	11.85 ± 9.22	11.99 ± 9.27	11.92 ± 9.25	4.17 ± 9.61	13.28 ± 8.42	9.85 ± 9.93
VO₂max, ml/kg/min	38.95 ± 6.30	37.06 ± 5.97	37.96 ± 6.20
Sit-up, n	33.92 ± 15.53	29.31 ± 15.72	31.66 ± 15.79
2-min step test, n				109.09 ± 23.15	102.23 ± 26.27	104.85 ± 25.34
30-s chair stand, n				21.31 ± 6.68	18.57 ± 6.35	19.60 ± 6.62
TUG, sec				6.01 ± 1.68	6.77 ± 28.15	6.48 ± 22.24

BMI; body mass index, Waist cir; waist circumference, TUG; time up and go, HGS; handgrip strength, Data are reported as the mean ± standard deviation.

Participants were stratified into adult and elderly groups. The average age of the adult participants was 39.06 years (± 14.59 SD), height was 166.28 cm (± 9.15 SD), and weight was 66.49 kg (± 12.74 SD); 49.08% of all the participants were female. The average age of the elderly participants was 72.73 years (± 5.44 SD), the average height was 157.29 cm (± 8.36 SD), and the average weight was 61.04 kg (± 9.41 SD); 62.32% of all the participants were female. The detailed characteristics of the participants are shown in Table 1.

Table 2

Regression equation predicting fitness age without outlier data by adults
		B	SE	β	t	P-value	R	Adjusted R²	SEE
Adults	(Constant)	100.882	.164		614.838	.000	.967	.936	3.634
	VO₂max	− .029	.005	− .013	-6.334	.000
	Relative HGS	-1.171	.002	− .932	-496.486	.000
	Sit-up	− .032	.002	.033	-17.175	.000
	Sit and reach	.032	.002	.020	14.911	.000
	Gender	.769	.039	.027	19.527	.000

Table 3

Regression equation predicting fitness age without outlier data by elderly individuals
		B	SE	β	t	P-value	R	Adjusted R²	SEE
Elderly	(Constant)	79.807	.201		397.012	.000	.493	.243	4.793
	2-min step test	− .017	.001	− .064	-20.041	.000
	Grip Strength	− .203	.003	− .281	-67.870	.000
	30-s chair stand	− .031	.003	− .031	-9.058	.000
	Sit and reach	− .052	.002	− .093	-28.053	.000
	TUG	.985	.012	.291	82.596	.000
	Gender	-3.468	.050	− .302	-69.217	.000

To develop the fitness age estimation formulas for Korean adults and the elderly individuals, this study conducted stepwise regression analysis with health-related fitness components as independent variables and age as the dependent variable. Table 2 shows the results of the multiple regression analysis using physical fitness parameters without outlier data. The explanatory power of the fitness age regression models (n = 359,303) for adults was 93.6%, and the SEE was 3.634 (t = 614.838, P < 0.001). Moreover, the explanatory power of the developed fitness age regression models (n = 142.471) for elderly individuals was 24.3%, and that for SEE was 4.793 (t = 397.012, P < 0.001) (Table 3). Finally, the formula for calculating fitness age is as follows:

(1) Fitness age for adults: 100.882 – (.029 × VO₂max) – (1.171 × Relative Grip Strength) – (.032 × Sit-up) + (.032 × Sit and reach) + (.769 × Gender _{male = 1; female = 2})

(2) Fitness age for elderly: 79.807 – (.017 × 2-min step test) – (.203 × Grip Strength) – (.031 × 30-s chair stand) – (.052 × Sit and reach) + (.985 × TUG) – (3.468 × Gender _{male = 1; female = 2})

Table 4

Correlation coefficients between dependent variables and fitness age parameters
		r	P-value
Adults
	VO₂max	− .637	P < .01
	Relative Grip Strength	− .956	P < .01
	Sit-up	− .616	P < .01
	Sit and reach	.011	P < .01
	Gender	.243	P < .01
Elderly
	2-min step test	− .271	P < .01
	Grip Strength	− .202	P < .01
	30-s chair stand	− .309	P < .01
	Sit and reach	− .250	P < .01
	Time Up and Go	.426	P < .01
	Gender	− .076	P < .01

Table 4 shows the relationships between age and the predicted fitness age parameters in the adult and elderly groups. The measured fitness age parameters were positively related to the predicted VO2max (r = − .637, P < 0.01), Relative Grip Strength (r = − .956, P < 0.01), Sit-up (r = − .616, P < 0.01), 2-min step test (r = − .271, P < 0.01), Grip Strength (r = − .202, P < 0.01), 30-s chair stand (r = − .309, P < 0.01), Time Up and Go (r = − .426, P < 0.01), sit and reach (r = .011, P < 0.01, r = − .250, P < 0.01, respectively), and gender (r = .243, P < 0.01, r = − .076, P < 0.01, respectively).

In this study, we examined the association between chronological age and physical fitness variables in the National Fitness Award Cohort study data and developed multiple linear regression analysis to predict fitness age using dependent variables (e.g., muscle strength, muscular endurance, cardiopulmonary endurance, flexibility, balance, agility). Statistical analysis was conducted by removing outliers to prevent an increase in prediction error before developing a multiple linear regression model to predict fitness age. As a result, the mean explanatory power of the predicted fitness age test regression model in adults was 93.6%, and the mean explanatory power of the predicted fitness age test regression model in the elderly individuals was 24.3%.

In recent decades, several studies have been reported since the concept of physical fitness age was proposed in the field of gerontology, and regarding aging and physical health, physical fitness age is a comprehensive evaluation of physical fitness rather than individual physical fitness levels [32]. Several studies have also reported that physical fitness age can be used to intuitively evaluate an individual's physical fitness [18, 33] and that physical fitness age is a way to make it easier to understand the physical fitness data of adults and elderly people, as well as a way to increase physical activity [12]. Several studies have been conducted to estimate physical age, and according to a report by M Kimura, C Mizuta, Y Yamada, Y Okayama and E Nakamura [18], a seven-year longitudinal study of 122 healthy elderly individuals aged 60 to 83 years determined five relevant physical fitness test results related to fitness age (i.e., grip strength, vertical jump, functional reach, one leg stand with eyes open, and 10 m walking time). In addition, EJ Latorre-Rojas, JA Prat-Subirana, X Peirau-Teres, S Mas-Alos, JV Beltran-Garrido and A Planas-Anzano [12] obtained a functional fitness age formula for 459 elderly females aged 50 to 89 years with the following six measures: the arm curl test, back scratch test, chair stand test, chair sit-and-reach test, and 8-foot up-and-go test. However, both studies were limited in their study due to their small sample size and could not include only females as participants or include elderly risk variables such as vertical jumps and arm-curls to generalize the measurement variables they used.

To compensate for the limitations of previous studies, this study used data from the National Fitness Award Cohort study of approximately 500,000 adults and elderly individuals from 2017 to 2021. In conclusion, we found health-related physical fitness indicators of fitness for fitness age (5 candidate markers for adults and 6 candidate markers for elderly individuals) using step-by-step selection methods and identified fitness indicators such as muscle strength, muscle endurance, cardiopulmonary endurance, flexibility, balance, and agility as health-related physical fitness component variables. This study predicted individual fitness age values from physical fitness indices for adults and elderly individuals, and the mean explanatory power of the fitness age regression model [100.882 – (.029 × VO2max) – (1.171 × Relative HGS) – (.032 × Sit-up) + (.769 × Gender Male = 1; Female = 2) + (.769 × Gender = 2)] was 93.6% (adjusted R2); [79.807 – (.017 × 2-minute step test) – (.203 × 30-second chair stand) – (.031 × 30-second chair stand) – (.052 × TUG) + (.985 × TUG) – (3.468 × Gender Male = 1; Female = 2)] was 24.3% (adjusted R2). The variables measured for each group were determined in advance by studies conducted by national institutions [34], and the researchers believe that a physical health assessment based on body age would be advantageous and useful for estimating an individual's physical health age.

Physical activity and physical fitness are known to be closely related [1, 2, 30], and some previous studies have reported that the difference between chronological age and physical fitness age reflects the progression of aging [26, 35]. Therefore, physical fitness is reportedly affected by factors such as lifestyle, physical activity level, and environmental conditions [14, 24], and because an individual's fitness age is affected not only by genetic factors but also by lifestyle, the level of physical fitness and fitness age differ different depending on the individual's management method. In this study, according to a multiple linear regression model of health-related fitness indicators, the age determinant of physical fitness in elderly individuals was found to be in a significantly lower-level range. Our study aimed to predict fitness age using only easy-to-measure independent variables, but the adjusted coefficient of determination was considered to be low and insufficient for use in clinical and medical fields. We assumed that if a prediction equation is developed that considers age and physical fitness variables for Korean adults and elderly participants, the physical fitness age of the Korean adults and elderly can be estimated more accurately. However, few scientifically rigorous studies are available, and systematic large-scale studies on the age-of-fitness estimation formula are limited. Therefore, a large-scale study is needed to expand upon these findings.

To the best of our knowledge, this study is the first large-scale study reported in Korea and is expected to present major trends and standardizations in future health-related fitness age analyses. Furthermore, this study is expected to serve as the foundation for customized exercise program services and for frailty classification via fitness variable analysis. This study has several limitations. First, participants in this study may not be representative of the sample. This is because adults and elderly individuals were voluntarily recruited according to their willingness, and the results could be biased. Second, biochemical and psychological parameters other than physical variables were not measured.

In conclusion, we selected health-related physical fitness indicators from among adults and elderly using a step-wise selection method and predicted the fitness age value of each group from these physical fitness indicators. The evaluation of fitness age based on health-related physical fitness indicators can indicate the level of physical aging and provide effective customized exercise programs suitable for physical fitness levels. Furthermore, it can be used as an indicator of longitudinal evaluation of the effectiveness of exercise programs and is expected to be used as a basis for exercise prescription programs along with exercise recommendations at the national level.

Ethics approval and consent to participate

Ethical approval of this retrospective study was given by the Institutional Review Board (IRB) of the Seoul National University Boramae Medical Center, and all subjects provided their written informed consent (IRB No. 07-2024-2).

Consent for publication

Not applicable.

Availability of data and materials

The complete data used throughout the study are available from the corresponding author upon reasonable request.

Competing interests

The authors declare that they have no conflict of interest.

Funding

This research was supported by Culture, Sports and Tourism R&D Program through the Korea Creative Content Agency (KOCCA) grant funded by the Ministry of Culture, Sports and Tourism (MCST) in 2023 (Project Name: Development of infrastructure linkage technology for community-based rehabilitation movements, Project Number: R202106001, Contribution Rate: 100%)

Author Contribution

Conceptualization, S-U.L.; Data curation, D.H.Y.; Funding acquisition, S-U.L.; Methodology, S-U.L. J-H.K and D.H.Y.; Project administration, S-U.L.; Writing-original draft, D.H.Y. and S-U.L.; Writing-review & editing, D.H.Y., J-H.K and S-U.L.

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No competing interests reported.

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You are reading this latest preprint version

A Study on the Development of a Fitness Age Prediction Model: The National Fitness Award Cohort Study 2017-2021

Status:

Journal Publication

Version 1

Abstract

Background

Methods

Results

Conclusions

INTRODUCTION

METHODS

Data Sets and Study Participants

Body composition

Muscle strength (Grip strength test)

Muscular endurance (30-s chair stand test and sit-ups)

Cardiopulmonary Endurance (2-min Step Test and Estimated VO_2max)

Flexibility (chair sit-and-reach)

Balance (time up-and-go)

Statistical analyses

RESULTS

DISCUSSION

CONCLUSION

Declarations

References

Additional Declarations

Status:

Journal Publication

Version 1

A Study on the Development of a Fitness Age Prediction Model: The National Fitness Award Cohort Study 2017-2021

Status:

Journal Publication

Version 1

Abstract

Background

Methods

Results

Conclusions

INTRODUCTION

METHODS

Data Sets and Study Participants

Body composition

Muscle strength (Grip strength test)

Muscular endurance (30-s chair stand test and sit-ups)

Cardiopulmonary Endurance (2-min Step Test and Estimated VO2max)

Flexibility (chair sit-and-reach)

Balance (time up-and-go)

Statistical analyses

RESULTS

DISCUSSION

CONCLUSION

Declarations

References

Additional Declarations

Status:

Journal Publication

Version 1

Cardiopulmonary Endurance (2-min Step Test and Estimated VO_2max)