Abstract
Objectives
The aim of this study is to compare occupational balance, physical fitness, and body composition parameters according to physical activity levels in older adults and to examine the relationships between occupational balance and other variables.
Methods
This cross-sectional study included a total of 150 older adults aged 65–75 years. Participants were classified according to their International Physical Activity Questionnaire-Short Form (IPAQ-SF) scores as inactive (n = 51), minimally active (n = 50), and active (n = 49). Occupational balance was assessed using the Occupational Balance Questionnaire-11 Turkish (OBQ11-T). Physical fitness level was assessed using the Timed Up and Go Test (TUG) and hand grip strength measured with a Jamar dynamometer (Sammons Preston, USA). For body composition assessment, skeletal muscle mass and body mass index (BMI) were measured using an InBody 120 device (InBody Co., Ltd., Seoul, Korea). Occupational balance, physical fitness, and body composition parameters were compared among older adults according to their physical activity levels. In addition, the relationships between occupational balance, physical activity, physical fitness, and body composition were examined in all participants.
Results
According to IPAQ-SF classification, significant differences were found in occupational balance, physical fitness, and body composition among inactive, minimally active, and active older adults (p < 0.001). Older adults with higher levels of physical activity demonstrated higher occupational balance, greater hand grip strength, greater skeletal muscle mass, shorter TUG durations, and lower BMI values. Regression model results showed that physical activity level, functional mobility, and skeletal muscle mass together explained approximately 52% of the total variation in occupational balance level (Adjusted R² = 0.520). In addition, physical activity level, TUG duration, and skeletal muscle mass were found to be independently associated with OBQ11-T scores (p < 0.05).
Conclusion
Older adults with higher levels of physical activity were found to have higher occupational balance levels, hand grip strength, functional mobility, and skeletal muscle mass, while their BMI values were lower. Furthermore, physical activity level, functional mobility, and skeletal muscle mass were found to be significantly associated with occupational balance. The findings indicate that occupational balance is an important parameter that should be assessed in older adults along with physical health, physical fitness, and activity participation.
Keywords: Older adults, Occupational balance, Physical activity, Physical fitness, Body composition
Introduction
Globally, demographics are changing rapidly, and the proportion of older adults is steadily increasing [1]. Many international organizations define individuals aged 65 years and older as older adults. According to World Health Organization data, the older population has increased significantly in recent years, and individuals aged 60 years and over are expected to account for approximately 22% of the global population by 2050 [2]. According to United Nations data, it is projected that by 2050, approximately one-sixth (around 16–17%) of the world’s population will be aged 65 and over [3].
As individuals age, various changes occur in physical, cognitive, sensory, and psychosocial areas [4]. Older adults commonly experience decreased muscle strength, balance impairments, declines in physical performance, reduced physical activity levels, and alterations in body composition [5]. Furthermore, the aging process may also be associated with declines in cognitive function, sensory losses, and reduced independence in activities of daily living. These changes may adversely affect individuals’ participation in daily life activities, ability to maintain life roles, and independent living skills [6].
It is emphasized that it is important not only to perform daily life activities, but also to maintain them within a meaningful, balanced, regular, and sustainable routine for the individual [7]. The literature indicates that it is important for individuals to perform self-care, productivity, rest, and leisure activities in a balanced and sustainable manner that is compatible with their individual activity roles [8]. In this context, the concept of occupational balance is defined as an individual’s ability to establish a satisfying, meaningful, and sustainable balance among their daily life activities. Occupational balance is reported to be related to well-being and daily living functionality [9].
Numerous studies in the literature have examined physical activity levels, physical performance, muscle strength, and body composition in older adults [10, 11]. These studies indicate that physical activity level is related to physical fitness, independent living skills, and activities of daily living [11, 12]. There are also studies that examine functional capacity as a result of changes in muscle strength, physical performance, and body composition during the aging process [13]. However, studies examining the concept of occupational balance in older adults tend to focus more on factors related to quality of life, well-being, depression, mental health, social participation, and activities of daily living [14–17]. Although previous studies have examined occupational balance in relation to well-being, health, activity participation, and daily functioning, limited evidence is available regarding its combined association with physical activity level, physical fitness, and body composition parameters in older adults. Therefore, the aim of this study is to compare occupational balance, physical fitness, and body composition parameters according to physical activity levels in older adults and to examine the relationships between these variables.
Methods
Study design
The study data were collected from older adults registered with the Turkish Retired Association, an organization with an extensive membership network across Turkey. The research process was conducted between January 2025 and April 2025. Ethical approval for this study was obtained from the Lokman Hekim University Scientific Research Ethics Committee (November 30, 2023; Decision No: 2023/216). All evaluations were conducted in person at the association’s headquarters in Ankara, in an environment arranged in accordance with research practices. All individuals participating in the study provided written informed voluntary consent, and the study was conducted in accordance with the principles of the Helsinki Declaration. Furthermore, in order to increase methodological transparency in the reporting of observational studies, the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines were taken into consideration in the preparation of this study [18].
Older adults participating in the study were primarily assessed using the International Physical Activity Questionnaire Short Form (IPAQ-SF). Participants were divided into three groups based on their IPAQ-SF results: inactive, minimally active, and active. The IPAQ-SF classification approach was selected because it provides clinically meaningful categories that are widely used in public health and aging research. Categorization enabled comparison of occupational balance, physical fitness, and body composition characteristics across established physical activity levels, facilitating interpretation of the findings from a clinical perspective. For group comparisons, IPAQ-SF was treated as a categorical variable using standard physical activity classifications (inactive, minimally active, and active). However, for correlation and regression analyses, the continuous IPAQ-SF MET-min/week score was used. This approach allowed both clinically meaningful group comparisons and the examination of associations across the full range of physical activity levels. Next, occupational balance, physical fitness, and body composition parameters were assessed in older adults across all groups. According to the evaluation results, the groups’ occupational balance, physical fitness levels, and body composition results were compared. In addition, the relationships between occupational balance and physical activity level, physical fitness, and body composition were examined in all older adults.
Participants
In the power analysis, which was performed by comparing occupational balance levels according to physical activity groups, a one-way analysis of variance (one-way ANOVA; fixed effects, omnibus, one-way) model within the F test family in the G*Power 3.1.9.6 program was used. Based on a moderate effect size (f = 0.30), a 95% confidence level (α = 0.05), and 80% test power (1-β = 0.80), the minimum sample size was calculated as 120 participants. Inclusion criteria were being 65 years of age or older and not having any cognitive or communication problems. Individuals with chronic diseases whose symptoms could not be controlled (cancer, neurological, orthopedic, rheumatological, cardiovascular, and cardiopulmonary diseases), those who had undergone surgery within the last 6 months, those unable to walk independently, those who had experienced limb amputation, and those using endoprosthetics were excluded from the study.
The study evaluated 160 older adults (57 inactive, 53 minimally active, and 50 active). However, 10 individuals (6 inactive, 3 minimally active, and 1 active) were excluded because they could not complete the evaluations, and the study was completed with 150 older adults (51 inactive, 50 minimally active, and 49 active).
Data collection
Participants’ age and gender information was recorded. The physical activity levels of older adults were assessed using the International Physical Activity Questionnaire Short Form (IPAQ-SF), and participants were classified according to their physical activity levels. Occupational balance was assessed using the Occupational Balance Questionnaire-11 Turkish Version (OBQ11-T). Physical fitness level was assessed using the Timed Up and Go Test (TUG) and hand grip strength measurements. The Jamar hand dynamometer was used to assess hand grip strength. Body composition was assessed using the InBody device, which measured skeletal muscle mass and Body Mass Index (BMI). All evaluations took approximately 25 min.
International Physical Activity Questionnaire-Short Form (IPAQ-SF)
The IPAQ-SF was developed in 1998 in Geneva by the International Consensus Group to assess individuals’ physical activity levels according to international standards [19]. The Turkish validity and reliability study of the questionnaire was conducted by Sağlam et al. [20]. This survey assesses individuals’ physical activity levels over the past seven days in four sections: vigorous physical activity, moderate physical activity, walking, and sitting time. When calculating the total score, the metabolic equivalent (MET) values for each activity [vigorous physical activity = 8 METs, moderate physical activity = 4 METs, walking = 3.3 METs] are multiplied by the duration (minutes) of the activity and the frequency of performance (number of days) to obtain the weekly MET-min score. An increase in the total MET-min score indicates a higher level of physical activity. Based on the IPAQ-SF scoring protocol, participants were classified into three categories: inactive, minimally active, and active. Individuals achieving at least 600 MET-min/week were classified as minimally active, whereas those achieving at least 1500 MET-min/week through vigorous physical activity on at least three days per week or at least 3000 MET-min/week through a combination of walking, moderate-intensity, and vigorous-intensity activities on seven or more days per week were classified as active [19].
Occupational Balance Questionnaire 11-Turkish (OBQ11-T)
OBQ11-T was developed by Wagman and Håkansson in 2014 to assess individuals’ levels of occupational balance [21]. The Turkish validity and reliability study of the scale was conducted by Günal et al. in 2019 [22]. The survey is used to assess the extent to which individuals balance their daily life roles, such as work, household chores, leisure activities, and sleep. The scale, consisting of 11 items in total, is scored on a 4-point Likert scale from “strongly disagree” (0 points) to “strongly agree” (3 points). The total score obtainable from the scale ranges from 0 to 33, with higher scores indicating a higher level of occupational balance [22].
Assessment of physical fitness level
The physical fitness tests used in this study were selected from commonly used and validated methods for assessing functional capacity in older adults. The Timed Up and Go (TUG) test is considered a reliable tool for assessing balance and functional mobility. Grip strength is considered an important indicator of overall muscle function and physical performance.
Timed Up and Go Test (TUG)
TUG is an objective, reliable, and easy-to-use measurement tool used to assess balance and functional mobility. The test was first developed by Podsiadlo and Richardson in 1991. During the test, participants were asked to get up from their chairs, walk 3 m, return to the starting point, and sit back down in the chairs. The test completion time was recorded in seconds. The evaluation was repeated three times, and the best time was considered in the analysis [23].
Handgrip strength assessment
The Jamar hand dynamometer (Sammons Preston, USA), recommended by the American Society of Hand Therapists (ASHT), was used to assess hand grip strength. Measurements were performed according to the recommendations of the American Society of Hand Therapists (ASHT). Participants were seated with the shoulder adducted and neutrally rotated, the elbow flexed at 90°, the forearm in a neutral position, and the wrist maintained in a neutral position. Three repetitions were performed with a one-minute rest period between measurements, and the highest value was used for analysis [24].
Assessment of body composition
Body composition assessments were performed using a bioelectrical impedance analyzer (BIA; InBody 120, InBody Co., Ltd., Seoul, Korea) with a tetrapolar 8-point tactile electrode system. Before the measurement, participants’ age, gender, and height information were recorded by the device. To improve measurement reliability, participants were asked to avoid caffeine and alcohol consumption 24 h prior to the test, to refrain from strenuous physical activity in the last 8 h, and to fast for at least 4 h before the measurement. Measurements were only taken from participants who met these conditions.
Participants were asked to clean their hands and feet before the measurement and were given a short rest period. All assessments were performed barefoot and in a standing position. All metal accessories were removed before the measurement, and participants were asked to stand upright, keep their arms slightly away from their bodies, and refrain from speaking during the measurement. With the feet positioned centered on the electrodes of the device, the hand electrodes were properly grasped, and this position was maintained throughout the measurement. Skeletal muscle mass (kg) and body mass index (BMI, kg/m²) values of older adults were obtained using the device [25].
Data analysis
Statistical analyses were performed using IBM SPSS Statistics 26 (Statistical Package for the Social Sciences, IBM Corp., Armonk, NY, USA) software. The normality of the data distribution was evaluated using the Kolmogorov–Smirnov test, skewness-kurtosis values, and histogram graphs. The analyses revealed that the numerical data showed a normal distribution. In descriptive statistics, numerical variables were expressed as mean ± standard deviation (Mean ± SD), and categorical variables were expressed as frequency (n) and percentage (%). In comparisons between the three groups (inactive, minimally active, and active) based on physical activity level, one-way analysis of variance (ANOVA) was used for numerical variables, and the chi-square (χ²) test was used for categorical variables. Post-hoc analyses were performed using the Tukey HSD test for variables where significant differences were found between groups. Pearson correlation analysis was used to evaluate the relationships between the variables. Reference values for the correlation coefficient (r) were considered as follows: 0.00–0.19 insignificant, 0.20–0.39 weak, 0.40–0.69 moderate, 0.70–0.89 strong, and 0.90–1.00 very strong [26]. In addition, effect sizes were interpreted according to conventional criteria. For eta-squared (η²), values of 0.01, 0.06, and 0.14 were considered small, medium, and large effects, respectively. For Cohen’s d, values of 0.20, 0.50, and 0.80 were interpreted as small, medium, and large effect sizes, respectively [27]. Multiple linear regression analysis was performed to examine the associations between physical activity level, physical fitness, body composition parameters, and occupational balance. Physical activity level (IPAQ-SF), TUG test duration, skeletal muscle mass, age, and sex were included in the regression model. Hand grip strength and BMI were not included in the final model because of their conceptual and physiological overlap with skeletal muscle mass and functional mobility variables. To avoid redundancy and potential multicollinearity, only one representative variable from each domain was included in the regression model. Prior to regression analysis, the assumption of multicollinearity was assessed using the variance inflation factor (VIF) and tolerance values. In addition, the assumptions of normality of residual values, linear relationship, and homogeneity of error variances were examined. In all analyses, the statistical significance level was accepted as p < 0.05.
Results
Descriptive information about the participants is presented in Table 1. The study included older adults aged 65–75 years with an average age of 68.29 ± 2.30 years. There were no statistically significant differences between the groups in terms of age and gender (p > 0.05).
Table 1.
Descriptive data of participants
| Inactive older adults | Minimally active older adults | Active older adults | ||
|---|---|---|---|---|
| n = 51 | n = 50 | n = 49 | p | |
| Mean±SD | Mean±SD | Mean±SD | ||
| Age (years) | 68.80±1.57 | 68.22±2.45 | 67.83±2.68 | a0.105 |
| Gender | ||||
| Female | 27 | 27 | 25 | b0.942 |
| Male | 24 | 23 | 24 |
n Sample size, SD Standard Deviation, a One-Way ANOVA, b Chi-square test, p < 0.05
Table 2 compares occupational balance, physical fitness, and body composition results of inactive, minimally active, and active older adult groups created according to their physical activity level. According to the analysis results, a statistically significant difference was found between the groups in terms of OBQ11-T scores (p < 0.001). Active older adults were found to have higher levels of occupational balance compared to other groups. When physical fitness parameters were examined, a significant difference was found between the groups in terms of TUG durations (p < 0.001), and it was determined that the active group had a shorter TUG duration. Hand grip strength values were found to increase as the level of physical activity increased (p < 0.001). Significant differences were also found between the groups in body composition results. The active group was found to have higher skeletal muscle mass and lower BMI values (p < 0.001).
Table 2.
Comparison of occupational balance, physical fitness, and body composition results among groups
| IPAQ-SF classification | Inactive older adults | Minimally active older adults | Active older adults | ||
|---|---|---|---|---|---|
| n = 51 | n = 50 | n = 49 | |||
| Mean±SD | Mean±SD | Mean±SD | p | η² | |
| IPAQ-SF | 330±37.20 | 765±170.93 | 2038.77±573.21 | < 0.001 | 0.819 |
| OBQ11-T | 11.60±2.89 | 17±3.53 | 26.46±4.30 | < 0.001 | 0.746 |
| Physical fitness | |||||
| TUG (sec) | 16.62±6.79 | 11.86±1.44 | 7.28±1.19 | < 0.001 | 0.468 |
| Hand grip strength (kg) | 12.07±2.98 | 19.92±2.38 | 24.28±2.91 | < 0.001 | 0.773 |
| Body composition | |||||
| Skeletal muscle mass (kg) | 21.45±3.24 | 26±1.81 | 29.55±3.47 | < 0.001 | 0.566 |
| BMI (kg/m²) | 32.52±3.95 | 30.34±2.17 | 25.48±2.09 | < 0.001 | 0.514 |
OBQ11-T Occupational Balance Questionnaire Turkish-11, IPAQ International Physical Activity Questionnaire, TUG Timed Up and Go Test, BMI Body Mass Index, n Sample size, SD Standard Deviation, sec Second, kg Kilogram, m² Square meter, η² Eta squared, p < 0.05
Table 3 presents the results of pairwise comparisons between groups formed according to physical activity level. According to post-hoc analysis results, statistically significant differences were found between groups in all parameters of occupational balance, physical fitness, and body composition (p < 0.001 for all comparisons). It was determined that the inactive, minimally active, and active groups of older adults all differed significantly from each other.
Table 3.
Results of pairwise comparisons between groups formed according to physical activity level
| IPAQ-SF classification | Inactive older adults- Minimally active older adults | Inactive older adults- Active older adults | Minimally active older adults- Active older adults | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| n = 51 | n = 50 | n = 49 | ||||||||||
| p | MD | SE | Cohen-d | p | MD | SE | Cohen-d | p | MD | SE | Cohen-d | |
| OBQ11-T | < 0.001 | -5.40 | 0.689 | 1.676 | < 0.001 | -14.86 | 0.693 | 4.072 | < 0.001 | -9.46 | 0.696 | 2.407 |
| Physical fitness | ||||||||||||
| TUG (sec) | < 0.001 | 4.76 | 0.817 | 0.965 | < 0.001 | 9.34 | 0.821 | 1.898 | < 0.001 | 4.58 | 0.825 | 3.464 |
| Hand grip strength (kg) | < 0.001 | -7.85 | 0.552 | 2.908 | < 0.001 | -12.21 | 0.554 | 4.145 | < 0.001 | -4.36 | 0.557 | 1.642 |
| Body composition | ||||||||||||
| Skeletal muscle mass (kg) | < 0.001 | -4.55 | 0.584 | 1.729 | < 0.001 | -8.10 | 0.587 | 2.415 | < 0.001 | -3.55 | 0.590 | 1.287 |
| BMI (kg/m²) | < 0.001 | 2.18 | 0.573 | 0.682 | < 0.001 | 7.04 | 0.576 | 2.215 | < 0.001 | 4.86 | 0.579 | 2.281 |
OBQ11-T Occupational Balance Questionnaire Turkish-11, IPAQ International Physical Activity Questionnaire, TUG Timed Up and Go Test, BMI Body Mass Index, MD Mean Difference, SE Standard Error, n Sample size, sec Second, kg Kilogram, m² Square meter, p < 0.05
Table 4 presents the correlation results between occupational balance and the parameters of physical activity, physical fitness, and body composition. According to the analysis results, IPAQ-SF, hand grip strength, skeletal muscle mass, and BMI demonstrated moderate statistically significant correlations with OBQ11-T, whereas TUG duration demonstrated a strong statistically significant correlation with OBQ11-T (p < 0.001). The findings show that occupational balance increases with increasing physical activity level, hand grip strength, and skeletal muscle mass; however, occupational balance decreases with increasing TUG duration and BMI.
Table 4.
Occupational balance, the examination of the relationship between physical activity, physical fitness, and body composition
| Older adults | ||
|---|---|---|
| n = 150 | ||
| OBQ11-T | ||
| p | r | |
| IPAQ-SF | < 0.001 | 0.630 |
| Physical fitness | ||
| TUG (sec) | < 0.001 | -0.718 |
| Hand grip strength (kg) | < 0.001 | 0.644 |
| Body composition | ||
| Skeletal muscle mass (kg) | < 0.001 | 0.629 |
| BMI (kg/m²) | < 0.001 | -0.570 |
OBQ11-T Occupational Balance Questionnaire Turkish-11, IPAQ International Physical Activity Questionnaire, TUG Timed Up and Go Test, BMI Body Mass Index, n Sample size, sec Second, kg Kilogram, m² Square meter, p < 0.05
Table 5 presents the results of a multiple linear regression analysis examining the associations between physical activity level (IPAQ-SF), functional mobility (TUG), skeletal muscle mass, age, sex, and occupational balance in older adults. Regression model results showed that the included variables explained approximately 52% of the total variation in occupational balance level (Adjusted R² = 0.520). After adjustment for age and sex, physical activity level, TUG duration, and skeletal muscle mass remained independently associated with OBQ11-T scores (p < 0.05). Higher physical activity level and greater skeletal muscle mass were independently associated with higher occupational balance scores, whereas longer TUG duration was independently associated with lower occupational balance scores.
Table 5.
Multiple linear regression analysis examining the associations of physical activity, functional fitness, body composition, age, and sex with occupational balance
| Older adults | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| (n = 150) | ||||||||||
| OBQ11-T | ||||||||||
| Unstandardized coefficients | Standardized coefficients | t | p | 95% CI (Lower–Upper) | Tolerance | VIF | Adjusted R² | p | ||
| B | SE | Beta | ||||||||
| Constant | 5.618 | 6.449 | 0.871 | 0.385 | -7.129-18.365 | |||||
| Age | 0.066 | 0.080 | 0.020 | 0.817 | 0.415 | -0.093-0.225 | 0.939 | 1.065 | 0.520 | < 0.001 |
| Gender | -0.432 | 0.359 | -0.028 | -1.204 | 0.231 | -1.140-0.277 | 0.995 | 1.005 | ||
| IPAQ-SF | 0.007 | < 0.001 | 0.675 | 9.443 | < 0.001 | 0.006–0.008 | 0.448 | 2.234 | ||
| TUG (sec) | -0.244 | 0.055 | -0.233 | -4.421 | < 0.001 | -0.353- -0.135 | 0.195 | 5.139 | ||
| Skeletal muscle mass (kg) | 0.169 | 0.074 | 0.127 | 2.275 | 0.024 | 0.022–0.316 | 0.173 | 5.782 | ||
OBQ11-T Occupational Balance Questionnaire Turkish-11, IPAQ International Physical Activity Questionnaire, TUG Timed Up and Go Test, SE Standard Error, VIF Variance inflation factor, n Sample size, sec Second, kg Kilogram; p < 0.05
Discussion
The main findings of the study showed that older adults with higher physical activity levels had higher occupational balance, better functional mobility, greater muscle strength, and greater skeletal muscle mass, whereas lower BMI values were observed in these individuals. Correlation analyses showed that increased physical activity level, muscle strength, and skeletal muscle mass were associated with increased occupational balance; while decreased functional mobility and increased BMI values were associated with decreased occupational balance. After adjustment for age and sex, physical activity level, functional mobility, and skeletal muscle mass remained independently associated with occupational balance and collectively explained approximately 52% of the variance in occupational balance scores.
Physical activity levels in older adults are reported to be associated with physical fitness, functional capacity, muscle strength, and body composition [28]. In particular, regular physical activity is noted to support muscle strength, mobility, and independent living performance in older adults [29]. Wickramarachchi et al. reported that physical activity plays an important role in maintaining functional independence in older adults [30]. Similarly, Chodzko-Zajko et al. reported that older adults with higher levels of physical activity had better physical performance and mobility outcomes [31]. Furthermore, studies examining the relationship between sarcopenia and physical inactivity emphasize that low levels of physical activity may be associated with decreased muscle mass and functional loss [32]. Previous studies have reported that older adults with higher levels of physical activity have greater hand grip strength and skeletal muscle mass, and lower BMI values [33]. Another study found that individuals with lower levels of physical activity also had lower participation in activities [34]. The present study showed that older adults with higher levels of physical activity had higher occupational balance levels, hand grip strength, functional mobility, and skeletal muscle mass, while their BMI values were lower. These findings suggest that higher physical activity levels may be associated not only with physical health parameters but also with occupational balance and participation in daily activities. Recent evidence has highlighted the broader health relevance of physical activity in older adults. Faraziani and Eken reported that higher levels of physical activity were associated with better quality of life and lower risk of cognitive decline in older adults [35]. These findings support the interpretation of occupational balance as a multidimensional health-related construct that may be associated not only with physical functioning but also with broader aspects of health and well-being. In addition, Sabbaghi et al. demonstrated that aquatic exercise improved body composition and motor performance in older men with sarcopenia [36]. Their findings support the importance of evaluating skeletal muscle mass, body composition, and functional mobility together when examining health-related outcomes in older adults. The magnitude of the between-group differences observed in the present study was relatively large. One possible explanation is that participants were classified according to established IPAQ-SF categories, resulting in groups with markedly different physical activity profiles. In addition, the study sample consisted of community-dwelling older adults who voluntarily participated in the study, which may have contributed to heterogeneity in physical performance and body composition characteristics. Particularly, the low hand grip strength values observed in the inactive group may reflect lower functional capacity and reduced physical activity participation within this subgroup. Therefore, these findings should be interpreted while considering the characteristics of the study sample and the recruitment process.
Although there are limited studies in the literature examining the relationships between occupational balance and physical health parameters [37, 38], it is observed that the relationships between activity participation, daily living activities and well-being have been investigated [16]. Furthermore, studies conducted on older adults have shown that physical parameters such as hand grip strength, mobility, and muscle mass are related to activities of daily living; and that a decrease in physical performance can negatively affect functional independence [39, 40]. Similarly, Leiros-Rodríguez and García-Soidan reported that balance-oriented exercise interventions improved functional performance in older women, highlighting the importance of mobility and balance-related capacities for maintaining functional independence in later life [41] .A recent study on adults aged 55–60 reported a significant relationship between lifestyle adaptation strategies, occupational balance, and physical activity level [37]. Huertas-Hoyas et al. found that physical and mental health in university students is related to occupational balance [38]. Håkansson et al. stated that occupational balance is related to an individual’s balanced participation in daily living activities and satisfaction with those activities [16]. A study conducted on older adults reported that hand grip strength, mobility, and muscle mass are related to daily living activities; and that a decrease in physical performance can negatively affect functional independence [39]. In our study, correlation analyses demonstrated moderate associations between occupational balance and physical activity level, hand grip strength, skeletal muscle mass, and BMI. In contrast, a strong association was observed between occupational balance and functional mobility as assessed by the TUG test. Higher physical activity levels, greater muscle strength, and greater skeletal muscle mass were associated with higher occupational balance, whereas longer TUG duration and higher BMI values were associated with lower occupational balance. The regression analysis results also showed that physical activity level, functional mobility, and skeletal muscle mass were significantly associated with occupational balance, and that these variables together explained approximately 52% of the variation in occupational balance scores. In the present study, physical activity was examined using both categorical and continuous approaches. While IPAQ-SF categories were used to compare outcomes across established physical activity levels, continuous IPAQ-SF scores were also analyzed through correlation and regression models to evaluate the strength and direction of associations. Therefore, the findings should be interpreted within the context of both complementary analytical approaches. This high level of explanatory power indicates that physical activity, physical capacity, and body composition are important variables associated with occupational balance in older adults. However, the cross-sectional nature of the study does not allow conclusions regarding causality or directionality. Furthermore, physical activity level and skeletal muscle mass were independently associated with occupational balance in older adults. These findings indicate that occupational balance in older adults is not limited solely to maintaining daily living routines; it is significantly related to multidimensional health components such as physical activity level, physical capacity, and body composition.
This study has some limitations. It only examined the associations between physical factors such as physical activity, physical fitness, body composition, and occupational balance; environmental, social, and psychosocial factors were not included in the evaluation. Therefore, residual confounding cannot be excluded. Other factors such as socioeconomic status, educational level, social support, environmental characteristics, mental health, and lifestyle-related variables may have influenced both occupational balance and physical activity-related outcomes. Furthermore, due to the cross-sectional design of the study, a causal interpretation is not possible. In addition, reverse causality cannot be excluded. While higher physical activity levels may be associated with better occupational balance, it is also possible that individuals with higher occupational balance engage more frequently in physical activity and maintain better physical fitness. In addition, participants were categorized according to IPAQ-SF physical activity classifications for group comparisons. Although this approach provides clinically interpretable categories, it may also introduce conceptual overlap because physical activity served as both the grouping variable and an explanatory variable in subsequent analyses. In addition, some degree of conceptual overlap may exist between occupational balance and physical activity. Occupational balance reflects participation and organization of daily occupations, whereas physical activity may constitute an integral component of many daily occupations. Therefore, part of the observed association between occupational balance and physical activity may reflect shared aspects of these related constructs rather than entirely independent phenomena. Future studies may benefit from examining these relationships using exclusively continuous physical activity measures. In addition, physical activity level was assessed using the self-reported IPAQ-SF. Although the IPAQ-SF is a widely used and validated instrument, self-reported physical activity measures may be subject to recall bias and overestimation, particularly among older adults. Therefore, the reported physical activity levels may not fully reflect actual activity behavior, which should be considered when interpreting the findings. It is recommended that future studies be planned as more comprehensive research examining different physical, environmental, social, and psychosocial factors that may affect occupational balance. Furthermore, the substantial differences observed between physical activity groups may partly reflect sample-specific characteristics and recruitment-related factors. In addition, all participants were recruited through the Turkish Retired Association. Individuals who are members of such organizations may differ from the general older adult population in terms of social engagement, activity participation, health status, and functional capacity. Therefore, the external validity and generalizability of the findings to the broader older adult population may be limited. Furthermore, the study sample primarily consisted of relatively functional community-dwelling older adults who were able to attend the assessment sessions and meet the study inclusion criteria. In addition, the exclusion of individuals with uncontrolled chronic diseases, recent surgery, inability to walk independently, amputation, and endoprosthesis use may have resulted in a study sample that was healthier and functionally more independent than the general older adult population. Therefore, the findings may not be directly generalizable to institutionalized older adults, individuals with substantial functional limitations, or those with more complex health conditions. Therefore, replication of these findings in different community-based older adult populations is warranted.
Conclusion
Older adults with higher levels of physical activity demonstrated higher occupational balance, better functional mobility, greater muscle strength, and more favorable body composition characteristics. Furthermore, physical activity level, functional mobility, and skeletal muscle mass were found to be significantly related to occupational balance. Higher physical activity levels and greater skeletal muscle mass were associated with higher occupational balance, whereas lower functional mobility and higher BMI values were associated with lower occupational balance.
In light of these findings, occupational balance, physical activity level, functional mobility, muscle strength, and body composition parameters should be considered together in the assessment of older adults. These findings may help inform future public health and community-based rehabilitation initiatives. However, longitudinal and interventional studies are needed to clarify the direction and causal nature of these associations. Given the observed associations between physical activity and occupational balance, future public health initiatives may consider strategies that support active participation and physical activity among older adults. However, these findings should be interpreted within the context of relatively functional community-dwelling older adults and may not be directly generalizable to all older adult populations.
Future studies are recommended to investigate the various physical, psychosocial, and environmental factors associated with occupational balance. It is also believed that studies conducted in different age groups and with different samples could contribute to the literature.
Acknowledgements
The authors thank all the older adults who participated in the study.
Ethical issues
The study protocol was approved by the Lokman Hekim University Scientific Research Ethics Committee (Decision no: 2023/216, Approval date: 30 November 2023,) and was carried out in accordance with the ethical rules established according to the Declaration of Helsinki.
Abbreviations
- ASHT
American Society of Hand Therapists
- BMI
Body mass index
- IPAQ-SF
International Physical Activity Questionnaire-Short Form
- OBQ11-T
Occupational Balance Questionnaire 11-Turkish
- m
Meter
- kg
kilogram
- %
Percentage
- n
Number of people
- SD
Standard deviation
- sec
Second
- STROBE
Strengthening the Reporting of Observational Studies in Epidemiology
- TUG
Timed Up and Go Test
Authors’ contributions
Conceptualization and methodology: ÖE and MC; Data collection: ÖE; Data analysis: MC Article writing and editing: ÖE and MC.
Funding
This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.
Data availability
The datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request.
Declarations
Ethics approval and consent to participate
Informed written consent was obtained from all participants.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request.