INTRODUCTION
Sarcopenia, characterized by an age-related decline in muscle mass and strength, is recognized as a major public health concern because it can lead to metabolic abnormalities, functional decline, hospitalization and increased mortality.1-4) These unfavorable consequences may impose significant direct and indirect financial burdens on individuals and society. According to Steffl et al.,5) the estimated mean annual direct and indirect healthcare costs for individuals with sarcopenia (EUR 1,125.3) were more than twice those for individuals without sarcopenia (EUR 561.4). Given that sarcopenia increases the risk of morbidity, mortality, and direct and indirect financial burdens, it is essential to develop strategies to prevent and manage sarcopenia, especially among those at high risk of sarcopenia.
The concept of possible sarcopenia (PS) has been proposed by the Asian Working Group for Sarcopenia (AWGS) and the European Working Group on Sarcopenia.6,7) The two working groups established a simple method to identify high-risk individuals with sarcopenia without advanced diagnostic equipment, such as dual-energy X-ray absorptiometry or bioelectrical impedance analysis, which includes handgrip strength (HGS) measurement and/or a 5-repetition sit-to-stand test.6,7) The validity and reliability of the concept of PS has been confirmed in many previous studies. Blanquet et al.8) reported that the PS concept is a valid practical way to screen for early-onset sarcopenia in acute care wards. Lim and Kong9) demonstrated that the diagnostic criteria of PS may be highly useful for screening adults with physical function decline or a high fall risk.
Physical inactivity has been recognized as a significant risk factor for sarcopenia. Gianoudis et al.10) reported that sedentary time is related to an increased risk of sarcopenia, with a 33% increase in risk for each 1-hour increase in daily sedentary time. Conversely, Zhao et al.11) reported that high intensity, frequency, duration and weekly volume of physical activity (PA) were associated with a decreased risk of PS in middle-aged and older adults. In addition, Westbury et al.12) reported that older adults with moderate-to-vigorous PA (MVPA) had a lower risk of sarcopenia than those without MVPA. Considering these studies, it is evident that PA, especially MVPA, is beneficial for preventing and ameliorating sarcopenia.
Daily MVPA can be classified into leisure-time and occupational domains.13-15) Generally, leisure-time PA involves dynamic movements at intensity levels sufficient to improve physical performance over a short period, assuming adequate recovery time.15) In contrast, occupational PA often includes static loading, monotonous and awkward working postures, and other nonconditioning activities for several hours per day with insufficient recovery time. While this type of activity may offer some health benefits, particularly for individuals who are otherwise sedentary, its positive effects are generally less substantial than those resulting from leisure-time PA.10,11,15) It is possible that MVPA may have different characteristics in the leisure-time and occupational domains; therefore, their relationships with PS may differ. The clarification of this potential difference between MVPA domains could lead to better PA strategies and public health policies for the prevention and management of PS. However, to date, no study has revealed the relationships between these two domain-specific PA categories and PS. Therefore, the present study explored the relationships between PS and PA in the leisure-time and occupational domains among middle-aged and older adults.
MATERIALS AND METHODS
Study Design and Participants
This population-based cross-sectional study used the Korea National Health and Nutritional Examination Survey (KNHANES) 2014–2018 database, which contains data on the general health, nutritional status, and lifestyle of the Korean population. The KNHANES targets a noninstitutionalized Korean population residing in South Korea, with a multistage clustered probability design.16) Specifically, of the 39,198 participants in the KNHANES 2014–2018, we included 15,819 adults aged ≥40 years. Participants aged ≤39 years were excluded because sarcopenia is a common geriatric syndrome that typically develops and progresses after the age of 40. A flow diagram of participant recruitment is displayed in Fig. 1. Each participant provided written informed consent. This study was conducted under the principles of the Declaration of Helsinki and approved by the Institutional Review Board of Changwon University (7001066-202207-HR-051).
Physical Activity and Possible Sarcopenia
PA, including leisure-time and occupational PA, and sedentary time were assessed using the Global Physical Activity Questionnaire, which has been used and recommended by the World Health Organization.17) The reliability and validity of the questionnaire have been verified in several countries, such as Bangladesh, France, and South Korea.18-21) Leisure-time PA included all sports, fitness, and recreational activities (e.g., brisk walking, cycling, swimming, and volleyball), whereas occupational PA included paid or unpaid work, household chores, food/crop harvesting, and related activities. Both domains were evaluated in terms of the frequency (day/wk) and duration (min/day) of moderate- or vigorous-intensity PA. Sedentary time was characterized by sitting or reclining at work, at home, traveling, or with friends, including time spent sitting at a desk, sitting with friends, traveling in vehicles, reading, playing cards or watching television, and it was evaluated as the time (minute) spent doing these activities. Since the World Health Organization or American College Sports Medicine’s PA guidelines recommend more than 30 min/day, 20 min/day and 150.0 min/week for moderate, vigorous, and MVPA, respectively, PA was classified into the following categories: (1) MVPA: no activity, 1–149 min/week, and ≥150 min/week moderate PA; (2) moderate PA: no activity, 1–29 min/day, and ≥30 min/day; and (3) vigorous PA: no activity, 1–19 min/day, and ≥20 min/day.22-24) HGS was assessed three times in each hand with maximum effort by using a dynamometer, and the mean of these three trials was adopted for each hand’s value. The mean of both hands was then adopted as the HGS value. In accordance with the AWGS 2019 guidelines,7) participants whose HGS was <28 kg for males and <18 kg for females were classified into the PS group, and the others were classified into the non-PS group.
Covariates and Other Parameters
Parameters that are potentially associated with sarcopenia or PA were adopted as covariates: age, sex, household income, educational level, medication use, alcohol consumption, smoking status, nutritional status and body weight.25,26) Household income was classified using tertiles. Self-reported alcohol consumption was categorized as never, ≤1 time/week, 2–3 times/week, or ≥4 times/week; education level was categorized as primary school, middle school, high school, college or beyond; and smoking status was categorized as never, former, or current smoker. Medication use, including hypertension, dyslipidemia, and types 1 and 2 diabetes medications, was recorded on the basis of a self-reported diagnosis by a physician. Nutritional data were gathered by utilizing a food frequency questionnaire composed of 63 food items that concern critical energy sources and nutrients.16) The questionnaire was designed as an open-ended survey for reporting a variety of dishes and foods utilizing the 24-hour recall method with various measuring aids.16) Height and body weight were assessed by utilizing a portable extensometer (InLabS50; InBody, Seoul, South Korea) and a digital scale (HE-58; CAS, Gyeonggi-do, South Korea), respectively.
Statistical Analysis
The chi-square test and the independent t-test or Mann‒Whitney U test were adopted for categorical and continuous parameters, respectively, to test the differences between the PS and non-PS groups. The results are presented as numbers and percentages or means±standard deviations. Logistic regression was employed on all the participants to evaluate the relationships of PS with leisure-time and occupational PA. The fully adjusted model was adjusted for age; sex; household income; medication use, including hypertension, dyslipidemia and type 1 or 2 diabetes medications; smoking and drinking habits; body weight; and nutritional information, including total calories and calories from carbohydrates, protein and fat. To evaluate the heterogeneity of the results, subgroup analyses were performed by sex, age (<65 or ≥65 years), and sedentary time (below or above the median), and the interaction effects of these factors were examined. Statistical significance was defined as p<0.05. All the statistical analyses were performed with SPSS version 26.0 (IBM Corp., Armonk, NY, USA).
RESULTS
A total of 39,198 participants participated in the KNHANES 2014–2018, but 23,379 participants were excluded for the following reasons: 14,439 participants aged < 40 years; 4,205 participants without HGS data; 788 participants without PA data; 321 participants without medication data; 104 participants without household income data; 3,269 participants without nutritional data; and 253 participants without other data. Overall, 15,819 participants, comprising 6,758 males and 9,061 females, were included in this study (Fig. 1).
The characteristics of the study participants are illustrated in Table 1. The mean HGS of all the study participants in the combined group and in the PS and non-PS groups were 28.1±9.7, 19.7±4.7, and 32.1±8.9, respectively. Compared with that in the non-PS group, the HGS in the PS group was lower (p<0.001). Among the 15,819 participants, 6,758 (42.7%) and 9,061 (57.3%) were males and females, respectively; 10,101 (63.9%) and 5,718 (36.1%) were middle-aged and older adults, respectively; and 7,521 (47.5%) and 8,298 (52.5%) were classified as having low and high levels of sedentary time, respectively. There were 1,594 (10.1%) and 2,328 (14.7%) participants with 1–149 min/week and ≥150 min/week of leisure-time MVPA, respectively, and there were 104 (0.7%) and 332 (2.1%) participants with 1–149 min/week and ≥150 min/week of occupational weekly MVPA, respectively. There were greater proportions of males and females in the PS group than in the non-PS group (p<0.001). There were lower and greater proportions of middle-aged and older adults, respectively, in the PS group than in the non-PS group (p<0.05). For leisure-time MVPA, higher or lower proportions were observed for no activity or for 1–149 min/week and ≥150 min/week, respectively, in the PS group (p<0.001). However, the proportions of sedentary time and occupational MVPA were not significantly different. Supplementary Table S1 contains additional characteristics of the study participants.
Table 2 compares the odds ratios (ORs) for the relationships of PS with leisure-time versus occupational MVPA. With respect to leisure-time MVPA, in the unadjusted model, compared with participants with no leisure-time MVPA, those with 1–149 min/week and ≥150 min/week had OR (95% confidence intervals [CI]) of 0.56 (0.50, 0.63) and 0.58 (0.52, 0.64), respectively (p<0.001 for both). According to the fully adjusted model, compared with those with no leisure-time MVPA, those with 1–149 min/week and ≥150 min/week of leisure-time MVPA had OR (95% CI) of 0.64 (0.54, 0.74) and 0.60 (0.52, 0.68), respectively (p<0.001 for both). However, the ORs for occupational MVPA were nonsignificant in both the unadjusted and fully adjusted models. Supplementary Table S2 lists daily MPA and VPA in total, leisure-time and occupational PA, and total weekly MVPA.
The sex-specific, age-specific, and sedentary-time-specific ORs for the relationships of PS with leisure-time and occupational MVPA are compared in Fig. 2. With respect to the relationships between PS and leisure-time MVPA, compared with those in the subgroup with no activity, the sex-specific ORs in the fully adjusted model were 0.71 (0.58, 0.86) and 0.65 (0.55, 0.77) for males and 0.53 (0.40, 0.70) and 0.51 (0.40, 0.64) for females in the 1–149 min/week and ≥150 min/week subgroups, respectively (p<0.01 for all). Compared with the no leisure-time MVPA subgroup, the age-specific ORs in the fully adjusted model were 0.62 (0.51, 0.76) and 0.59 (0.50, 0.70) for middle-aged adults and 0.66 (0.50, 0.86) and 0.60 (0.48, 0.75) for older participants in the 1–149 min/week and ≥150 min/week subgroups of leisure-time MVPA, respectively (p<0.01 for all). The sedentary-time-specific ORs in the fully adjusted model were 0.77 (0.60, 0.99) and 0.55 (0.34, 0.89) for low-sedentary-time participants and 0.56 (0.46, 0.69) and 0.50 (0.41, 0.60) for high-sedentary-time participants in the 1–149 min/week and ≥150 min/week subgroups of leisure-time MVPA, respectively, relative to the no-leisure-time MVPA subgroup (p<0.05 for all). However, no significant ORs were observed for the relationships between PS and occupational MVPA. All subgroup analyses stratified by sex, age, and sedentary time revealed no significant heterogeneity (all p-values for interactions >0.05) (Supplementary Tables S3, S4, and S5 list the sex-specific, age-specific, and sedentary-time-specific total weekly MVPA, as well as daily MPA and VPA in total, leisure-time, and occupational PA. Supplementary Table S6. sex-, age- and sedentary time-specific unadjusted odds ratios for the relationships of PS with leisure versus occupational PA, corresponding to Fig. 2).
DISCUSSION
This population-based cross-sectional study is the first to explore the associations of PS with leisure-time versus occupational MVPA among middle-aged and older adults. The findings suggest the following: (1) for leisure-time MVPA, compared with subjects with no MVPA, those with 1.0–149.0 min/week and ≥150.0 min/week had ORs of 0.64 (0.54, 0.74) and 0.60 (0.52, 0.68), respectively (p<0.001 for both); (2) occupational MVPA was not related to PS; and (3) these findings were consistent when the analyses were stratified by sex, age and sedentary time. These novel findings demonstrate that PS is inversely associated with leisure-time PA but is not related to occupational PA.
These novel findings demonstrate that PS is inversely related to leisure-time PA but may not be inversely related to occupational PA among middle-aged and older adults. The positive influences of PA on health have been well documented for decades.27,28) However, since the domain-specific influences of PA on health have yet to be adequately reported, the World Health Organization guidelines encourage further investigations concerning domain-specific influences on health.29,30) Concerning this issue, Holtermann and his colleagues13,14,31) proposed the concept of the PA paradox, arguing that occupational PA does not confer health benefits in the same manner as leisure-time PA. Moreover, other recent studies have demonstrated that leisure-time PA had a favorable influence on metabolic syndrome, hypertension, cardiovascular events, and all-cause mortality, but there was an unfavorable or no relationship between occupational PA and these health conditions.15,32,33) Conversely, Aune et al.34) and Sattelmair et al.35) reported that occupational PA, as well as leisure-time PA, had preventive effects on health conditions, including type 2 diabetes and coronary heart disease, even though leisure-time PA had greater effects than occupational PA. Considering the inconsistency regarding the PA paradox among these previous studies, it is difficult to determine whether occupational PA confers health benefits.
The main criticism of the PA paradox has been the insufficient adjustment for potential covariates. To address this issue, we adjusted the logistic regression analysis among all study participants for age, sex, household income, medication use, drinking and smoking habits, and weight or nutritional status. As a result, we demonstrated that the relationship between leisure-time MVPA and PS was independent of these covariates (Table 2). In contrast, occupational MVPA had an insignificant relationship with PS, which was consistent regardless of the adjustment (Table 2). To avoid the potential bias caused by study participant characteristics, we also stratified the analyses by sex, age and sedentary time while adjusting for the covariates mentioned above. The subgroup analyses (Fig. 2) supported the robustness of these results. Moreover, daily leisure-time MPA of 1.0–29.0 min/day and ≥30.0 min/day and VPA of 1.0–19.0 min/day and ≥20.0 min/day were inversely related to PS (Supplementary Tables S3–S5).
These findings are consistent with those of Rosique-Esteban et al.,36) who reported that as MPA, VPA, and MVPA increased to 60 min/day, the ORs of sarcopenia were 0.80, 0.51, and 0.75, respectively, in older adults with obesity according to a fully adjusted model that included sex, age, study center, smoking and drinking habits, diabetes status, body mass index, protein intake, and corresponding time spent on other PA or sedentary time. However, given the differences in study populations (older adults with obesity vs. middle-aged and older adults with or without obesity) and study outcomes (sarcopenia vs. possible sarcopenia), direct comparisons should be made with caution. In light of these differences, we interpret the findings of Rosique-Esteban et al.36) as supportive evidence for an inverse association between leisure-time PA and PS in the current study.
One possible explanation for the observed inverse association is that leisure-time MVPA, which involves dynamic muscle contractions performed at conditioning intensities, may provide an anabolic or neuromuscular stimulus.15,37,38) Kim and Kim38) demonstrated that both resistance training and aerobic exercise increase motor unit conduction velocity, indicating electrophysiological adaptations of muscle fiber membrane properties. As the quantity and intensity of leisure-time MVPA increase, regardless of modality, the muscle stimulus may also increase. Furthermore, adequate recovery time could support adaptive responses in the muscles.15) However, given the cross-sectional nature of this study, these mechanisms should be considered as generating hypotheses rather than establishing causal relationships.
On the other hand, owing to the lack of studies exploring the relationship between occupational PA and PS, it is difficult to compare our findings directly with those of previous research. However, many types of occupational PA, characterized by static loading, monotonous and awkward postures, and insufficient recovery time,15) may generally provide an inadequate anabolic stimulus, including insufficient conditioning intensity and a lack of progressive overload, to preserve or enhance the musculoskeletal system.37,38) Moreover, under such conditions, a high occupational workload combined with limited recovery may induce sustained inflammatory responses, which could contribute to chronic musculoskeletal pain, physical deterioration, and functional impairment.39-41) To mitigate these risks, regulating task intensity and recovery time during the working day should be considered.31) Nevertheless, given the considerable heterogeneity in occupational PA across different job types—for example, physically demanding work such as farming or construction—caution is warranted in generalizing these findings to all forms of occupational activity.
The main strength of the present study is the examination of the relationships of PS with leisure-time versus occupational MVPA among middle-aged and older adults. Our study findings can serve as a reference for the development of domain-specific PA guidelines and sarcopenia prevention strategies. Another strength is the sex-specific, age-specific, and sedentary-time-specific analysis, which could address the potential bias due to different participant characteristics. We also accounted for potential confounders to PS or PA to mitigate any criticism related to the PA paradox. However, this study has several limitations. First, this study employed a cross-sectional design, which precludes the establishment of cause-and-effect associations. To confirm the findings in the present study, further longitudinal studies will be helpful. Second, only approximately 2.8% of participants reported any occupational MVPA. This low prevalence may limit the statistical power and precision of our findings. Moreover, domain-specific PA was assessed using the self-reported GPAQ, which is susceptible to recall bias and intensity misclassification. Accordingly, the results regarding occupational MVPA should be interpreted with caution. Future studies with larger occupationally active samples and device-based, objective measures are warranted to increase statistical power. Third, our study focused on Korean individuals, but Caucasians and Africans exhibit different anthropometric, cultural, and occupational characteristics. Thus, further research is needed to confirm the findings of the present study in different ethnic groups.
In conclusion, leisure-time PA has an inverse relationship with PS, but occupational PA has no relationship with PS, suggesting that PA programs that specifically focus on leisure-time PA may be necessary to prevent and manage possible sarcopenia.
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