Abstract
The KOJI AWARENESS (KA) screening test assesses motor function in humans. We aimed to analyze the correlation between age and KA screening scores and to identify the specific age at which a significant decline occurs. A total of 793 healthy participants (234 females) were interviewed for basic information on age and sex and completed the KA screening test. In addition to calculating the total score from the KA test, the scores were calculated for the neck-scapula-upper extremity-complex (NSU), trunk, and lower extremity (LE) segments. Spearman’s rank correlation coefficient was used to assess the validity of the test. Additionally, Bayesian linear regression was employed to estimate the change point in KA scores, facilitating the identification of a critical age associated with a notable decline in motor function. KA screening total and separate body segment scores were negatively correlated with age in both gender (for female and male, KA total score, ρ = − 0.443, ρ = − 0.344; NSU segment, ρ = − 0.431, ρ = − 0.427; trunk segment, ρ = − 0.210, ρ = − 0.473; LE segment: ρ = − 0.43, ρ = − 0.507). Furthermore, a change-point analysis using linear regression analysis showed that KA screening total scores declined sharply at the age of 49.1(95% credible interval: 37.503, 68.366). The result show that total KA scores decrease − 0.196 (95% credible interval: − 0.335, − 0.049) for every 1 year of age increase, and for ages over 49.1, total KA scores additionally decrease − 0.255 (95% credible interval: − 0.485, − 0.054) for every 1 year of age increase. In the NSU segment, females showed a more rapid decline than males from the age of 50 years. KA screening test total scores declined sharply at the age of 49.1. These results may be useful in setting treatment goals, exercise, and lifestyle programs for age-related decline in motor function.
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Introduction
Increasing elderly population rates are a major social issue that require urgent attention1,2. Studies show a resultant decline in activities of daily living with age, which is attributed to diminished motor function3,4,5,6,7,8,9. Preventing an age-related decline in motor function associated with aging is, therefore, of paramount importance. Physicians and physical trainers are actively involved in exercise programs aimed at preserving motor function. Therefore, understanding the age at which motor function begins to significantly decline is crucial for effective interventions.
Motor function is associated with muscle strength, flexibility, and balance. Range of motion typically starts to decline at approximately 60 years of age10; muscle strength declines from the age of 40 to 50 years11,12,13, and the rate of muscle mass decline peaks at approximately 45 years14. However, few studies have elicited robust results regarding age of decline in motor function based on a comprehensive assessment.
The KOJI AWARENESS (KA) test is a comprehensive assessment of motor function, including muscle strength, flexibility, and balance ability15,16,17,18. The intraclass correlation coefficients (1,1) of this test was 0.876 (95% CI, 0.434–0.981), indicating high reliability16. We analyzed the correlation between this test and the Functional Movement Screening, a tool used widely around the world to evaluate motor function15. As a result, a significant correlation was found between the two tests (r = 0.609, p < 0.001), even when age was taken into account, confirming the validity of this test15. Furthermore, a lower KA test has predicted the risk of motor function injuries in a previous long-distance runner study18. Using the KA test, it is possible to determine changes in motor function associated with aging. The test has proven reliability and validity and is a simple and rapid method for assessing motor function without the need for specialists who have completed a certification workshop and without specific equipment15. However, the KA test has not been used to evaluate motor function in a large number of participants across a wide range of ages, and the changes in KA scores with age are unknown.
We, therefore, aimed to analyze the association between age and KA scores and to determine the peak age of motor function decline (change point). We hypothesized that KA scores would decrease with advancing age, and that the score would sharply decline near 40–50 years of age. Understanding this critical age of decline is crucial, as it can motivate healthcare professionals to design customized exercise programs, which play a pivotal role in encouraging individuals to actively engage in activities to maintain and enhance overall wellbeing.
Materials and methods
Participants
The design of this study was a cross-sectional study conducted at a single center. Our study was conducted at fitness centers affiliated with the authors’ institutions under the guidance of the Department of Joint Surgery and Sports Medicine, Graduate School, from October 2019 to March 2022. Ethical approval was obtained from the Research Ethics Committee of the affiliated institutions (research protocol identification number: M2019-168), followed the principles of the Declaration of Helsinki (52nd World Medical Association General Assembly Edinburgh, Scotland, October 2000) for medical research involving human subjects. To recruit participants for this study, posters were posted in the gym, sports environment, and sports field of the facility where the researcher is affiliated. In addition, subjects were recruited through exercise instructors related to the researcher. All participants provided written informed consent before the KA screening test.
We determined the sample size based on the number of participants who participated in the study during the study period. Participants were excluded for the following reasons: those who had sustained an injury within the past 3 months, those who discontinued the measurement due to pain or injury, those with a history of orthopedic disease, those who were restricted or prohibited from participating in sports for medical reasons, and those with a history of surgery. In total, 793 participants aged < 90 years were included. Participants were instructed to wear comfortable athletic attire to allow free movement. The participants were also instructed to stop the test if they experienced any pain during execution. However, none of the participants discontinued their participation because of injuries or pain during the course of the study.
KA self-screening movement test
Detailed information regarding the KA self-screening movement test is provided in Appendix 1. Athletes used a checklist to self-evaluate the functionality of each body part, comprising 11 components in total. Each component has specific scoring criteria, with a maximum total score of 50 (Table 1). The self-scoring method was thoroughly explained to the participants, and verbal confirmation was obtained to ensure a complete understanding of the process (Appendix 1. The participants then proceeded to self-rate the motor function of each item according to the method provided.
The participants were evaluated each action three times, and their best scores were recorded15,16,17,18. Screening and the protocol were monitored by certified athletic trainers and chiropractors to ensure accurate scoring. On average, participants completed the assessment within 20 min.
Distinct scoring components were assigned to each area to segment (Neck-scapula-upper extremity-complex, Trunk, Lower extremity) the body parts for screening. The neck-scapula-upper extremity-complex (NSU) segment included items to evaluate the mobility and muscle strength of the upper extremities, including the neck and scapula. The trunk segment included items to evaluate the muscle strength and stability of the core, as well as its mobility, including the thoracic spine. The lower extremity (LE) segment included items to evaluate the mobility, muscle strength, and supportive function of the lower extremities. The NSU segment was defined as the sum of neck mobility, shoulder mobility, shoulder blade mobility, and upper extremity stability and strength; the Trunk segment was the sum of thoracic spine mobility, upper extremity stability and strength, hip and spine mobility, and upper and lower extremity mobility and stability, mid-section stability and strength; the LE segment component was defined as a total score of hip mobility, hip and spine mobility, upper and lower extremity mobility and stability, lower extremity strength, and ankle mobility. Upper extremity stability and strength, hip and spine mobility, upper and lower extremity mobility and stability involve the following functions: the upper limbs and trunk, the trunk and lower limbs, and the trunk and lower limbs, respectively. Therefore, these items were added together for each segment to calculate the score. The total scores were 14, 22, and 26 points for the NSU, trunk, and LE, respectively.
Statistical analyses
First, differences in age, KA, and body segment scores between genders were analyzed using Mann- Whitney test. Second, Spearman’s correlation analysis was performed to examine the relationship between KA scores and age. Spearman’s rank correlation coefficient was then conducted to analyze the relationship between age and KA score, body segments (NSU and Trunk, LE), categorized as ‘‘strong’’ (ρ > 0.5), ‘‘medium’’ (0.5 < ρ < 0.3), or ‘‘weak’’ (0.3 < ρ < 0.1)19.
We performed a change-point (cp) analysis to identify the cutoff point for age at which a decline in the KA score occurred. The score was analyzed by the linear regression model and included age–cp, sex, interaction between age–cp and I (age ≥ cp), and interaction between (age–cp) and sex as independent variables, whereby I (age ≥ cp) is the indicator function (e.g., if the age of the participant, \(i\), was higher than the value of the cp, then I [age ≥ cp] = 1, otherwise, I [age ≥ cp] = 0). The parameters in the model were estimated based on the Bayesian theorem. Noninformative prior distributions were used for each parameter. The posterior distribution of each parameter was computed using the Markov chain Monte Carlo procedure. We estimated the posterior means and 95% credible intervals (CrI) for the coefficients of the model. More detailed information on the abovementioned analysis is provided in Supplementary Appendix 2.
Using the posterior mean of cp (\(\widehat{cp}\)), we compared the age trends before and after \(\widehat{cp}\) for the NSU, trunk, and LE. We analyzed KA score by the linear regression model with (age–\(\widehat{cp}\)), sex, interaction between (age–\(\widehat{cp}\)) and I (age ≥ \(\widehat{cp}\)), and interaction between (age–\(\widehat{cp}\)) and sex as independent variables for each body segment. Statistical significance was defined as p < 0.05. Statistical analyses were performed using SPSS software (version 21.0; IBM Corp, Armonk, NY) and SAS version 9.4 (SAS Institute Inc., Cary, NC).
Results
A total of 723 participants were included in the analysis. Seventy participants were excluded based on the exclusion criteria. The ages of the participants and their respective scores are presented in Table 2. The median (interquartile range) age of the participants was 23.0(18.0) years (Table 3). There were significant difference in ages between the gender (Female: 20.0[9.0], Male: 35.0[23.0], p < 0.001). The median (interquartile range) scores of the NSU, Trunk, and LE segments and total KA scores were 12.0 (4.0), 20.0 (5.0), 23.0(6.0), and 44.0 (9.0), respectively (Table 3). The relationships between age and each score are presented in Table 4. The NSU and trunk, LE segments of KA score, and total scores showed significant negative correlations with age (ρ = − 0.457, − 0.477, − 0.346, − 0.506, respectively; p < 0.001). Age and each score were significantly negatively correlated, even when sex was analyzed separately, and the strength of the correlations did not differ (Table 4).
Our cp analysis showed that KA scores declined sharply at the age of 49.1 and the results were similar when analyzed by sex (Fig. 1). Furthermore, when we compared the trend in age before and after 50 years for each body component, the KA scores for the NSU and LE declined rapidly after 50 years (Fig. 2). The posterior mean (95%CrI) of the coefficient for age was − 0.196 (− 0.335, − 0.049), and that for I (age ≥ cp) was − 0.255 (− 0.485, − 0.054). The result mean that for ages under 49.1, total KA scores decrease 0.196 for every 1 year of age increase, and for ages over 49.1, total KA scores decrease 0.451 (= 0.196 + 0.255) for every 1 year of age increase. For both the trunk and LE, there were no differences in the degree of decline in KA scores between the gender. However, for the NSU segment, there were sex differences. Female scores dropped more sharply than male scores (Fig. 2).
The change point of KA total scores. KA, KOJI AWARENESS. Blue and red lines represent the mean for males and females, respectively.
The change point of KA scores for each body segment. A, NSU segment. B, Trunk segment. C, LE segment. The blue and red lines represent the mean for males and females, respectively. KA, KOJI AWARENESS; NSU, neck-scapula-upper extremity-complex; LE, lower extremity.
Discussion
We examined the correlation between age and KA scores to assess age-related changes in motor function and identify the pivotal age of peak decline. The results showed a significant negative correlation between KA screening scores and age. A negative correlation was also observed between KA scores for specific body segments and age. Furthermore, a negative correlation was found when participants were analyzed separately by sex. A change-point analysis indicated a sharp decline in KA scores at the age of 49.1, with consistent results across gender. Furthermore, when examining the change point of each body segment at 50 years, the KA scores for the NSU and LE exhibited a rapid decline. Specifically, the scores for the NSU segment showed a more pronounced decrease in females compared to males.
One of our study purposes was to determine the peak age of motor function decline (change point), and we wanted to explore the change point by data-driven method. The change point analysis is the method that detects change point in data in a data-driven manner and simultaneously analyzes the trends before and after the change point. Therefore, we applied the change point analysis because we considered it suitable for our study purpose.
A negative correlation was observed between age and the total KA score. Muscle strength14,20,21,22 and flexibility10,23,24,25 tend to decline with age. Balance also deteriorates with aging, becoming one of the factors contributing to an increased risk of falls and fear of falling26,27,28. Our results support the findings of these previous studies.
A negative correlation was observed between age and separate body segments (NSU, trunk, and LE) in relation to KA scores. Muscle strength and mass in the lower11,29,30,31,32 and upper33 limbs tend to decrease with age. Additionally, with aging, there is an increase in the stiffness of soft tissues such as muscles and tendons34, leading to a decrease in joint range of motion10,35,36. The force exerted by core muscles, an important aspect of muscle function, tends to decline with age37,38. These declines in various functions are associated with performance variables such as balance, walking speed, standing speed, and motor abilities39,40, as well as the risk of falls41,42,43. The results of the present study support these findings. Furthermore, our results introduce new data, showing that not only the total KA score but also the scores for each body segment are correlated with age.
Our cp analysis revealed a sharp decline in the KA scores at 49.1 years of age. The decline in range of motion typically begins around the age of 60 years10, while muscle strength starts declining between the ages of 40 and 50 years11,12,13. Muscle mass begins to decline and reaches a peak at approximately 45 years of age14. The present study supports previous reports that scores begin to decline at a certain age. On the other hand, the difference in the age at which the decline occurs between the results of this study and those of previous studies may be due to the different items being evaluated. The KA screening test scores comprehensive motor function, including muscle strength, range of motion, and balance, and the results of this study reveal new findings that KA scores decline rapidly at 49.092 years.
For gender differences, from past studies, in both upper and lower limb strength, it has been demonstrated that the decline in muscle strength with age is more gradual in females compared to males44,45,46,47. Furthermore, the decline in physical strength with age is more gradual in females compared to males48. Changes in the range of the upper extremities, neck, and trunk due to aging are minimally influenced by gender49. In the present study, NSU segment scores decreased more rapidly in women than in men. The results of this study may have differed from previous reports. The scores evaluated in this study were scores that comprehensively evaluated motor functions such as muscle strength and range of motion, and there have been no reports analyzing age-related changes by gender. The difference in the items evaluated is thought to be a factor that caused the difference between the results of past studies and this study. Specifically, the KA scores in the NSU, which focus on motor function of neck-scapula-upper extremity-complex, demonstrated a sharp decline in females compared to males, particularly around the age of 50 years. Therefore, the present study provides new findings by examining the change in slope based on the change scores by gender.
Regarding clinical implications, age-related functional changes include increased passive resistance in elastic tissues within muscles, tendons, and joint structures; weakening of primary mover muscles; and disturbances in proprioceptive control, among other factors50,51. Using the KA screening test to assess functional decline caused by these factors may provide a basis for interventions aimed at maintaining and improving physical function with age, ultimately preventing disability52. Additionally, understanding site-specific scores can help identify the areas of the body that are experiencing decline, which can be useful to tailor exercise guidance to target specific aspects of motor function.
There is a cp at which motor function begins to decline before the age of 50 years. Individuals are required to consciously maintain and improve motor function from an early stage. For individuals aged 50 and above, it is important to encourage active lifestyles, including various exercises aimed at slowing down the decline in motor function. Furthermore, we revealed a significant, rapid decline in NSU motor function in females starting from the age of 50 years, emphasizing the importance of conscious strengthening and engaging the upper body through daily physical activities and exercise interventions for females.
Our study has some limitations. First, this study was not designed to examine differences across generations through paired comparisons with an equal number of groups, but rather to conduct a statistical evaluation of the relationship between KA scores and age. Therefore, the sample sizes across different generations were not balanced. Second, this is a cross-sectional study. Since the participants were not observed and analyzed longitudinally, it was impossible to determine the causal relationship between age and changes in KA scores. We aim to conduct further studies to clarify the relationship between age and the KA score by increasing the number of participants and analyzing differences between generations, as well as by analyzing changes in longitudinal KA scores. Third, the distribution of participants across age categories were skewed. The estimates in age groups with a large number of cases were stable but estimates in age groups with a small number of cases were an interpolation of the results of other age groups, and the accuracy of the estimates may be lower than in other age groups. Therefore, given that the data in this study is limited for elderly people, the accuracy of the estimate of the slope above the change point might be lower than that of the estimate of the slope below the change point. Particularly, considering that the number of participants in the 50–59 age category was only 21, we assumed that there was limitation to apply the results of this study to population in their 50 s in practical settings. Finally, there were age differences by gender in the participants. In present study, females were younger than males, indicating an age bias by gender. Since KA scores decrease with age, younger participants had higher KA scores than older participants, which may affect the estimated inflection point of aging. Based on the above limitations, it is necessary to further increase the number of subjects in their 50 s and beyond, as well as to include more female participants in future analyses. In addition, it is important to measure longitudinal data trends and analyze the causal relationship between age and KA score.
Conclusions
We revealed a significant negative correlation between age and KA screening scores, highlighting a decline in motor function associated with aging; our cp analysis identified a sharp decline at 49.1 years. These findings suggest that the KA screening test is a valuable tool to evaluate motor function, set treatment goals, and contribute to exercise and lifestyle programs.
Data availability
The datasets generated and/or analysed during the current study are not publicly available due permission for secondary use of the data has not been obtained through ethical review but are available from the corresponding author on reasonable request.
Code availability
The research presented in this manuscript does not involve using custom code or mathematical algorithms central to the conclusions.
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Acknowledgements
We extend our heartfelt appreciation to Dr. James Parker for his invaluable cooperation in this study. We also acknowledge the support from the Japan Agency for Medical Research and Development (AMED) grant, under Grant Number JP23le0110027.
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KM: Conceptualization, Methodology, Writing- Original draft preparation. HK: Data curation, SM: Visualization, Investigation, Project administration. KH: Writing- Reviewing and Editing, Validation. HF: Data analysis, Resources, RH: Data analysis, Software, Writing figure 1-2. AH: Data analysis, KY and KK: Supervision. All authors reviewed the manuscript.
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Murofushi, K., Katagiri, H., Mitomo, S. et al. Exploring age-related changes in motor function: insights from the peak decline found in Koji Awareness screening test. Sci Rep 14, 18903 (2024). https://doi.org/10.1038/s41598-024-69971-7
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DOI: https://doi.org/10.1038/s41598-024-69971-7
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