ABSTRACT
Objectives
Sarcopenia, an age‐related muscle disease, is influenced by a variety of factors, making it essential to explore the associations of physical activity, psychological health, employment, and exercise with this condition in a healthy, well‐nourished population.
Method
This study was conducted with 766 healthy adults to assess these relationships based on EWGSOP2. Participants completed the IPAQ and provided a work and exercise history. Mental health was evaluated using BDI‐13 and DASS‐21.
Result
Physical activity, both current and past, was significanly associated with a lower odds of sarcopenia. Employment status and type of work were also associated with odds of sarcopenia. Participation in sports and exercise duration were linked to decreased odds (OR = 0.687 (95% CI = 0.600–0.787), p < 0.005), with specific activities like swimming and certain traditional Iranian sports showing strong associations. Psychological factors such as scores of depression (OR = 1.149 (95% CI = 1.112–1.189), p < 0.005), anxiety (OR = 1.233 (95% CI = 1.176–1.293), p < 0.005), and stress (OR = 1.084 (95% CI = 1.056–1.114), p < 0.005) were associated with an increased odds of sarcopenia. The multivariable analyses confirmed the independent relationships of physical activity and some mental health components with sarcopenia. Predictive models highlighted the importance of physical activity, exercise, and employment status as key variables associated with sarcopenia.
Conclusion
This study revealed that higher work engagement, better mental health components (including lower stress, depression, and anxiety), higher levels of physical activity and, longer duration of exercise, and engaging in specific types of exercise may be related to lower odds of sarcopenia.
Keywords: mental health, physical activity, sarcopenia
This study highlights that physical activity, employment, and mental health significantly influence sarcopenia risk in healthy adults. Working part‐time or full‐time and exercising, especially sports like swimming, alongside good psychological health, may be associated with reduced odds of sarcopenia.
1. Introduction
Sarcopenia, a multifaceted muscle disease, is characterized by the loss of muscle strength, power, and mass [1, 2]. It negatively impacts health, increasing risks for falls, frailty, fractures, morbidity, reduced quality of life, and mortality [1, 2]. The prevalence of sarcopenia worldwide remains largely undiagnosed, although it affects approximately 8%–36% of the population < 60 years, and 10%–27% in ≥ 60 years [3, 4, 5]. Moreover, sarcopenia often remains undiagnosed, a situation projected to worsen [4, 5].
Age is recognized as a primary risk factor; however, emerging evidence indicates that physical activity and mental health are also significant predictors of sarcopenia [1, 2, 6, 7, 8, 9]. Inactivity is critical in promoting sarcopenia, while exercise serves as a promising intervention for its prevention and management [1, 2, 5, 6, 10]. Numerous studies confirm a positive correlation between exercise and muscle mass in healthy older adults [11]. Resistance training, in particular, effectively combats sarcopenia by stimulating muscle protein synthesis and promoting growth, thereby preventing muscle loss, enhancing muscle quality, and improving function [2, 12, 13, 14]. Nevertheless, optimal exercise parameters remain unclear, and further investigation into long‐term exercise interventions in aging populations is essential.
Interestingly, individuals with sarcopenia demonstrate a higher incidence of mental health disorders, although findings are inconsistent [15, 16, 17, 18]. Some studies indicate a link between sarcopenia and psychiatric disorders such as depression [19, 20]. However, it is still unclear whether sarcopenia has an independent influence on depressive symptoms beyond the effect of other contributing factors [21]. Although some research counters this connection [18]. Thus, further exploration is necessary to clarify the relationship between sarcopenia and mental health.
Given the emerging links between sarcopenia and mental health, along with the importance of exercise, this study aims to investigate the association between current and past physical activity, exercise, work, and mental health regarding sarcopenia in healthy older adults in Iran. The objective is to identify potential risk factors and contribute to the establishment of a sarcopenia registry as a feasibility study.
2. Materials and Method
2.1. Participation
This cross‐sectional feasibility study, approved by Mashhad University of Medical Sciences (Ethical Code: IR.MUMS.MEDICAL.REC.1401.657), examines the relationship between mental health, physical activity, and sarcopenia in 766 healthy individuals aged 55 or older.
2.2. Inclusion Criteria
Age: 55 years or older
Residency: At least 10 years in Mashhad
Health: Absence of serious illnesses affecting metabolic stability, including but not limited to cardiovascular disease, chronic kidney disease, and uncontrolled diabetes.
No intellectual, psychiatric disorders, or neurodevelopmental conditions.
2.3. Exclusion Criteria
Malnutrition, assessed via the Mini Nutrition Assessment–Short Form, to eliminate bias [22].
Any physical disability preventing standing.
Unreported diseases relevant to the inclusion criteria.
Presence of metals affecting Bioelectrical Impedance Analysis (BIA).
Data inaccuracies.
A complete overview of diagnostic methods and protocols can be found elsewhere [3].
2.4. Evaluation and Diagnosis of Sarcopenia
Sarcopenia diagnosis followed the European Working Group on Sarcopenia in Older People 2 (EWGSOP2) guidelines. The diagnostic criteria are as follows [6]:
2.4.1. Diagnostic Criteria
Pre‐sarcopenia: Low muscle strength only.
Sarcopenia: Low skeletal muscle mass index (SMMI) and low muscle strength.
Severe Sarcopenia: Low SMMI, low muscle strength, and low gait speed.
Muscle strength was evaluated using a hydraulic hand dynamometer, with the average of three trials recorded. Muscle mass was derived via BIA, and SMMI was calculated as SMM/height [3]. Gait speed was determined through a 4‐m walking test, involving three trials with participants commencing from a standing position, allowing for familiarization. The best time recorded determined gait speed in meters per second (m/s) [2, 6].
2.4.2. Cut‐Off Points
Women: SMMI < 5.5 kg/m2, muscle strength < 16 kg, gait speed < 0.8 m/s.
Men: SMMI < 7 kg/m2, muscle strength < 27 kg, gait speed < 0.8 m/s.
2.5. Assessment of Physical Activity, Sport, and Working Status
Physical activity was gauged using the International Physical Activity Questionnaire (IPAQ), focusing on metabolic equivalents (METs), which was validated for the Iranian population [23]. Each participant completed the questionnaire for current and retrospective physical activity levels between the ages of 30 and 45. Given the lack of direct measures for past activity, the IPAQ's structure allowed a reasonable approximation. Changes in physical activity were categorized as increased, decreased, or unchanged based on a threshold of 25% MET. Physical activity levels were categorized according to no specific physical activity as very low (< 300 MET‐min/week), low‐moderate (300–600 MET‐min/week), moderate (600–1500 MET‐min/week), and over‐moderate (> 1500 MET‐min/week).
These categories were applied to both current and retrospective data to assess longitudinal patterns. Participants also reported their work history, specifying occupation type and weekly activity durations for exercises such as walking and swimming, confirming exercise speeds based on their gait speed test results. Certain thresholds excluded slow walking from exercise classifications, yet still factored into overall IPAQ estimates.
2.6. Mental Health Assessment
The study considered the risk of undiagnosed mental health conditions, despite excluding major disorders. Mental health was evaluated using the Beck Depression Inventory (BDI‐13) [24] and the Depression Anxiety Stress Scales (DASS‐21) [25] under an independent psychologist's supervision. The BDI‐13, tailored for the Iranian population, scored between 0 and 3 per question, classifying depression risk accordingly. The DASS‐21 consists of 21 multiple‐choice questions to assess anxiety, stress, and depression risk. The main reasons to include two separate tools for the assessment of mental health were related to the validity of each tool and the aims of the main study.
2.7. Statistical Analysis
Statistical evaluations employed IBM SPSS Statistics version 20.0, R, and IBM SPSS Modeler. The distribution of residuals was assessed via the Shapiro–Wilk test and visual inspection of Q–Q plots for normality. A p‐value > 0.05 indicated a normal distribution. Levene's test determined variance homogeneity. Data presentation favored percentages for categorical variables and means ± SD for continuous ones. The one‐way ANOVA or Mann–Whitney U test compared continuous variables, while the chi‐square test assessed categorical variables across sarcopenia groups. Binary logistic regression calculated odds ratios (OR) between normal and sarcopenic groups. ANCOVA examined independent variable effects on sarcopenia while adjusting for covariates, and the Chi‐Square Automatic Interaction Detector (CHAID) method performed decision tree analytics. A Bayesian network analysis was used to model the relationships between variables using IBM SPSS Modeler. A p‐value < 0.05 was deemed statistically significant.
3. Result
This study involved 766 participants, with a female‐to‐male ratio of 2:1, a mean age of 64.91 ± 7.12 years, a weight of 72.59 ± 11.9 kg, and a BMI of 26.6 ± 4.44 kg/m2 (Table 1). Sarcopenia status is categorized into four groups: normal (73%), pre‐sarcopenic (23.9%), confirmed sarcopenia (1.8%), and severe sarcopenia (1.3%) [3]. Differences in work and physical activity levels were statistically significant among these groups (p < 0.01).
TABLE 1.
The demographic data of the population included in the study (n = 766).
| Count (n) | Valid n % | ||
|---|---|---|---|
| Gender | Male | 256 | 33.4% |
| Female | 510 | 66.6% | |
| Age | 64.91 ± 7.12 | ||
| Education | Up_to_high_school | 241 | 31.5% |
| High‐school | 130 | 17.0% | |
| Diploma | 250 | 32.6% | |
| BS | 81 | 10.6% | |
| MS | 30 | 3.9% | |
| PhD | 34 | 4.4% | |
| House ownership | Rent | 133 | 17.4% |
| Owned | 633 | 82.6% | |
| Income Classification | None | 252 | 32.9% |
| Lower | 132 | 17.2% | |
| Same level | 279 | 36.4% | |
| Higher | 103 | 13.4% | |
| Health insurance | Yes | 704 | 91.9% |
| No | 62 | 8.1% | |
| Marriage | Married | 601 | 78.5% |
| Single | 12 | 1.6% | |
| Devoice | 17 | 2.2% | |
| Dead_of_other | 136 | 17.8% | |
| Sleep | 7.01 ± 1.58 | ||
| Smoke | No | 582 | 76.0% |
| Past | 90 | 11.7% | |
| Yes | 94 | 12.3% | |
| Alcohol | Yes | 4 | 0.5% |
| No | 749 | 97.8% | |
| Past | 13 | 1.7% | |
| Weight | 72.59 ± 11.9 | ||
| BMI | 26.6 ± 4.44 | ||
Note: For numeric (parametric) data, the statistics are reported as mean and standard deviation (SD).
Current physical activity levels significantly associated to odds of sarcopenia. Individuals engaged in low‐moderate (OR = 0.366, 95% CI = 0.242–0.555, p < 0.005), moderate (OR = 0.064, 95% CI = 0.038–0.108, p < 0.005), and above moderate physical activity (OR = 0.023, 95% CI = 0.007–0.076, p < 0.005) showed lower odds of sarcopenia compared to those with very low physical activity (Table 2). Furthermore, past physical activity was also a significant related factor; individuals with stable (OR = 0.342, 95% CI = 0.236–0.496, p < 0.005) or increased activity levels (OR = 0.15, 95% CI = 0.074–0.303, p < 0.005) exhibited reduced odds of sarcopenia, emphasizing the importance of an active lifestyle.
TABLE 2.
The association of physical activity criteria of the population with sarcopenia classification by EWGSOP2 (2018).
| Factors | Values | Overall (n = 766) | Groups | |||||
|---|---|---|---|---|---|---|---|---|
| Normal (n = 559) | Sarcopenia (n = 207) | O.R (95% CI) | p | |||||
| Pre‐sarcopenia (n = 183) | Confirmed‐sarcopenia (n = 14) | Severe‐sarcopenia (n = 10) | ||||||
| Currently working | No | 563 (73.4%) | 389 (69.6%) | 153 (83.6%) | 12 (85.7%) | 9 (90%) | Ref | 0.010 |
| Part‐time | 85 (11.0%) | 71 (12.7%) | 13 (7.1%) | 1 (7.1%) | 0 (0.0%) | 0.441 (0.242–0.804)* | ||
| Full‐time | 118 (15.4%) | 99 (17.7%) | 17 (9.3%) | 1 (7.1%) | 1 (10.0%) | 0.429 (0.254–0.723)** | ||
| Employment history | No history or housewife | 350 (45.6%) | 231 (41.3%) | 117 (63.9%) | 1 (7.1%) | 1 (10.0%) | Ref | < 0.001 |
| Retired | 218 (28.4%) | 159 (28.4%) | 40 (21.9%) | 11 (78.6%) | 8 (80.0%) | 0.720 (0.497–1.045) | ||
| Working | 198 (25.8%) | 169 (30.2%) | 26 (14.2%) | 2 (14.3%) | 1 (10.0%) | 0.333 (0.212–0.523)** | ||
| Kind of work | No/homeworker | 339 (44.2%) | 220 (39.4%) | 117 (63.9%) | 1 (7.1%) | 1 (10.0%) | Ref | < 0.001 |
| Transportation services | 48 (6.2%) | 29 (5.2%) | 17 (9.3%) | 1 (7.1%) | 1 (10.0%) | 1.211 (0.652–2.252%) | ||
| Educational | 50 (6.5%) | 39 (7%) | 11 (6.0%) | 0 (0.0%) | 0 (0.0%) | 0.521 (0.258–1.056) | ||
| Medical | 24 (3.1%) | 8 (1.4%) | 10 (5.5%) | 3 (21.4%) | 3 (30.0%) | 3.697 (1.537–8.892)* | ||
| Military | 28 (3.6%) | 27 (4.8%) | 1 (0.5%) | 0 (0.0%) | 0 (0.0%) | 0.068 (0.009–0.510)* | ||
| Commercial services | 54 (7.0%) | 51 (9.1%) | 1 (0.5%) | 1 (7.1%) | 1 (10.0%) | 0.109 (0.033–0.356)** | ||
| Office work | 172 (22.4%) | 145 (25.9%) | 18 (9.8%) | 6 (42.9%) | 3 (30.0%) | 0.344 (0.216–0.549)** | ||
| Academic | 25 (3.2%) | 24 (4.3%) | 0 (0.0%) | 1 (7.1%) | 0 (0.0%) | 0.077 (0.010–0.577)* | ||
| Labor/cleaning | 26 (3.33%) | 16 (2.9%) | 8 (4.4%) | 1 (7.1%) | 1 (10.0%) | 1.155 (0.508–2.626) | ||
| Current physical activity | Very low | 158 (20.6%) | 63 (11.3%) | 83 (45.4%) | 7 (50.0%) | 5 (50.0%) | Ref | < 0.001 |
| Low‐moderate | 236 (30.8%) | 152 (27.2%) | 75 (41.0%) | 6 (42.9%) | 3 (30.0%) | 0.366 (0.242–0.555)** | ||
| Moderate | 283 (36.9%) | 258 (46.2%) | 22 (12.0%) | 1 (7.1%) | 2 (20.0%) | 0.064 (0.038–0.108)** | ||
| Over moderate | 89 (11.6%) | 86 (15.4%) | 3 (1.6%) | 0 (0.0%) | 0 (0.0%) | 0.023 (0.007–0.076)** | ||
| Past physical activity | Very low | 94 (12.2%) | 47 (8.4%) | 43 (23.5%) | 3 (21.4%) | 1 (10.0%) | Ref | < 0.001 |
| Low‐moderate | 373 (48.6%) | 268 (47.9%) | 90 (49.2%) | 7 (50.0%) | 8 (80.0%) | 0.392 (0.247–0.623)** | ||
| Moderate | 280 (36.5%) | 227 (40.6%) | 48 (26.2%) | 4 (28.6%) | 1 (10.0%) | 0.233 (0.141–0.386)** | ||
| Over moderate | 19 (2.4%) | 17 (3.0%) | 2 (1.1%) | 0 (0.0%) | 0 (0.0%) | 0.118 (0.026–0.538)* | ||
| Physical activity change in time | Decrease | 161 (21.0%) | 85 (15.2%) | 66 (36.1%) | 5 (35.7%) | 5 (50.0%) | Ref | < 0.001 |
| Same | 512 (66.8%) | 392 (70.1%) | 107 (58.5%) | 8 (57.1%) | 5 (50.0%) | 0.342 (0.236–0.496)** | ||
| Increase | 93 (12.1%) | 82 (14.7%) | 10 (5.5%) | 1 (7.1%) | 0 (0.0%) | 0.151 (0.074–0.303)** | ||
| Exercises activity | Yes | 388 (50.6%) | 305 (54.6%) | 80 (43.7%) | 2 (14.3%) | 1 (10.0%) | Ref | < 0.001 |
| No | 378 (49.3%) | 254 (45.4%) | 103 (56.3%) | 12 (85.7%) | 9 (90.0%) | 1.794 (1.297–2.479)** | ||
| Kind of exercises | No | 377 (49.2%) | 253 (45.3%) | 103 (56.3%) | 12 (85.7%) | 9 (90.0%) | Ref | 0.002 |
| Swim | 67 (8.7%) | 57 (10.2%) | 10 (5.5%) | 0 (0.0%) | 0 (0.0%) | 0.358 (0.177–0.725)** | ||
| Walking | 248 (32.3%) | 183 (32.7%) | 63 (34.4%) | 2 (14.3%) | 0 (0.0%) | 0.725 (0.508–1.034) | ||
| Aerobic | 44 (5.7%) | 36 (6.4%) | 7 (3.8%) | 0 (0.0%) | 1 (10.0%) | 0.453 (0.205–1.005) | ||
| Ancient Iranian exercises | 8 (1.0%) | 8 (1.4%) | 0 (0.0%) | 0 (0.0%) | 0 (0.0%) | — | ||
| Heavy exercises | 22 (2.8%) | 22 (3.9%) | 0 (0.0%) | 0 (0.0%) | 0 (0.0%) | — | ||
| Duration of exercises | h/week | 1.33 ± 1.9 | 1.56 ± 2.1 | 0.74 ± 0.93 | 0.64 ± 1.77 | 0.14 ± 0.44 | 0.687 (0.600–0.787)** | < 0.001 |
Note: Frequency of population in each cell percentile according to the outcome (Sarcopenia) of the population.
OR (adjusted for age and gender) compared between Normal and Sarcopenia Group (Pre‐Sarcopenia, Sarcopenia and Severe‐Sarcopenia). For nominal variables, O.R reported in comparison with the first row. In continuous variables, rows with more than two values in nominal variables continue the effect of increasing value compared with the first row.
Effect estimates with a p‐value < 0.05 are indicated in bold for both O.R and p‐value. The p‐value of OR, is not reported in this table.
p‐value: ANOVA or Chi‐Square between groups. Confirmed‐Sarcopenia (n = 14) and Severe‐Sarcopenia (n = 10) due to low sample size were considered as one group during statical analysis.
The heavy exercise includes routine football, basketball, bodybuilding, and mountain climbing at least 2 times per week.
p‐value < 0.05 reported * and < 0.005 reported as **. Insignificant values are not reported.
In physical activity levels, participants with a daily MET ≤ 1.3, 1.3 to 2.1, 2.1 to 3, and > 3 were categorized as having very low, low‐moderate, moderate, and over‐moderate physical activity levels, respectively.
Employment status was significantly associated with odds of sarcopenia. Part‐time (OR = 0.441, 95% CI = 0.242–0.804, p < 0.05) and full‐time workers (OR = 0.429, 95% CI = 0.254–0.723, p < 0.005) had lower odds of sarcopenia compared to unemployed or retired individuals. Interestingly, no significant difference was found between retirees and those with no work history or homemakers. The type of employment also influenced sarcopenia risk; former military (OR = 0.068, 95% CI = 0.009–0.51, p < 0.05), commercial services (OR = 0.109, 95% CI = 0.033–0.356, p < 0.005), and academic professionals (OR = 0.077, 95% CI = 0.01–0.577, p < 0.05) showed significant protective relationship, whereas those in medical services exhibited the highest odds ratio (OR = 3.697, 95% CI = 1.537–8.892, p < 0.05). No significant association was observed for transportation and labor/cleaning jobs.
Exercise participation and duration were significantly correlated with odds of sarcopenia (Table 2). Individuals not engaging in any exercise had a higher odds of sarcopenia (OR = 1.794, 95% CI = 1.298–2.480, p < 0.005). Additionally, each hour of exercise was associated with a 32% reduction in odds of sarcopenia (OR = 0.687, 95% CI = 0.600–0.787, p < 0.005). Among the exercises, swimming was notably linked to lower odds of sarcopenia (OR = 0.358, 95% CI = 0.177–0.725, p < 0.005). All participants engaged in traditional Iranian exercises and high‐intensity sports such as football and mountain climbing were found to be non‐sarcopenic, although other sports did not yield statistically significant results.
Mental health assessments employing the BDI‐13 and the DASS‐21 revealed a significant association with sarcopenia, based on EWGSOP2 criteria (Table 3). BDI‐13 scores were positively correlated with odds of sarcopenia (OR = 1.149, 95% CI = 1.112–1.189, p < 0.005). Similarly, anxiety (OR = 1.233, 95% CI = 1.176–1.293, p < 0.005) and stress levels (OR = 1.084, 95% CI = 1.056–1.114, p < 0.005) were associated with increased sarcopenia likelihood, underlining the significance of mental health in this demographic. Nevertheless, the depression association based on DASS‐21 was not statistically significant, despite a significant difference in categorized depression levels, confirming prior BDI‐13 findings.
TABLE 3.
The association of mental health criteria of population with sarcopenia classification by EWGSOP2 (2018).
| Questionnaires | Values | Overall | Groups | p | ||||
|---|---|---|---|---|---|---|---|---|
| Normal (n = 559) | Sarcopenia (n = 207) | O.R (95% CI) | ||||||
| Pre‐sarcopenia (n = 183) | Confirmed‐sarcopenia (n = 14) | Severe‐sarcopenia (n = 10) | ||||||
| BDI‐13 score | Depression score | 5.96 ± 5.27 | 4.88 ± 4.73 | 8.19 ± 4.96 | 10.93 ± 5.12 | 18.3 ± 7.78 | 1.149 (1.112–1.189)** | < 0.001 |
| Depression classification (BDI‐13%) | Normal | 322 (42.0%) | 292 (52.2%) | 29 (15.8%) | 1 (7.1%) | 0 (0.0%) | Ref | < 0.001 |
| Low | 254 (33.1%) | 178 (31.8%) | 73 (39.9%) | 1 (7.1%) | 2 (20.0%) | 4.159 (2.619–6.601)** | ||
| Moderate | 131 (17.1%) | 59 (10.6%) | 61 (33.3%) | 10 (71.4%) | 1 (10.0%) | 11.88 (7.136–19.773)** | ||
| High | 59 (7.7%) | 30 (5.4%) | 20 (10.9%) | 2 (14.3%) | 7 (70.0%) | 9.409 (4.992–17.732)** | ||
| DASS‐21 score | Depression score | 9.64 ± 6.56 | 9.76 ± 6.41 | 8.94 ± 5.65 | 7.07 ± 9.41 | 18.88 ± 12.67 | 0.989 (0.961–1.019) | < 0.001 |
| Anxiety score | 8.54 ± 5.26 | 7.34 ± 4.35 | 12.52 ± 6.02 | 14.17 ± 5.19 | 17.33 ± 4.08 | 1.233 (1.176–1.293)** | < 0.001 | |
| Stress score | 14.97 ± 8.17 | 13.82 ± 7.89 | 18.36 ± 7.59 | 23 ± 8.2 | 28.5 ± 4.23 | 1.084 (1.056–1.114)** | < 0.001 | |
| Depression classification (DASS‐21) | Normal | 222 (42.6%) | 208 (50.9%) | 14 (13.9%) | 0 (0.0%) | 0 (0.0%) | Ref | < 0.001 |
| Low | 144 (27.6%) | 122 (29.8%) | 21 (20.8%) | 1 (16.7%) | 0 (0.0%) | 2.679 (1.322–5.431)* | ||
| Moderate | 110 (21.1%) | 51 (12.5%) | 54 (53.5%) | 3 (50.0%) | 2 (33.3%) | 17.189 (8.899–33.198)** | ||
| High | 45 (8.6%) | 28 (6.8%) | 11 (10.9%) | 2 (33.3%) | 4 (66.7%) | 9.017 (4.013–20.279)** | ||
| Anxiety classification (DASS‐21) | Normal | 240 (46.1%) | 216 (52.8%) | 24 (23.8%) | 0 (0.0%) | 0 (0.0%) | Ref | < 0.001 |
| Low | 139 (26.6%) | 101 (24.7%) | 36 (35.6%) | 2 (33.3%) | 0 (0.0%) | 3.389 (1.928–5.918)** | ||
| Moderate | 89 (17.1%) | 60 (14.7%) | 25 (24.8%) | 2 (33.3%) | 2 (33.3%) | 4.346 (2.359–8.018)** | ||
| High | 53 (10.1%) | 32 (7.8%) | 15 (14.9%) | 2 (33.3%) | 4 (66.7%) | 5.912 (2.951–11.823)** | ||
| Stress classification (DASS‐21) | Normal | 253 (48.5%) | 231 (56.5%) | 22 (21.8%) | 0 (0.0%) | 0 (0.0%) | Ref | < 0.001 |
| Low | 119 (22.8%) | 96 (23.5%) | 21 (20.8%) | 2 (33.3%) | 0 (0.0%) | 2.516 (1.338–4.719)* | ||
| Moderate | 87 (16.6%) | 41 (10.0%) | 41 (40.6%) | 3 (50.0%) | 2 (33.3%) | 11.781 (6.420–21.618)** | ||
| High | 62 (11.9%) | 41 (10.0%) | 16 (15.8%) | 1 (16.7%) | 4 (66.7%) | 5.378 (2.711–10.653)** | ||
Note: Frequency of population in each cell percentile according to the outcome (Sarcopenia) of the population.
Effect estimates with a p‐value < 0.05 are indicated in bold for both O.R and p‐value. The p‐value for O.R (95% CI), is not reported in this table.
OR(adjusted for age and gender) compared between Normal and Sarcopenia Group (Pre‐Sarcopenia, Sarcopenia and Severe‐Sarcopenia). In continuous variables, and for rows with more than two values in nominal variables, the effect of increasing value is compared with the first row.
p‐value: ANOVA or Chi‐Square between groups. Confirmed‐Sarcopenia (n = 14) and Severe‐Sarcopenia (n = 10) due to low sample size were considered as one group during statical analysis.
p‐value < 0.05 reported * and < 0.005 reported as **.
Abbreviations: BDI‐13 Beck Depression Inventory 13 items; DASS‐21: Depression Anxiety Stress Scales 21‐item; SF‐36: Medical Outcomes Study Health Survey Questionnaire 36‐Item Short Form.
To account for potential confounders, identified associated factors were organized into five models (Table 4). After adjustments, the models reinforced that nearly all physical activity and mental health components were independently linked to sarcopenia. The regression decision tree model (Figure 1) identified changes in physical activity over time as the most significant predictor of sarcopenia. For individuals with decreased activity, exercise duration emerged as a important predictors, while previous activity levels and current employment status were key predictors for those whose activity levels remained unchanged. The Bayesian network model (Figure 2) highlighted primary work, current employment status, and physical activity changes as crucial predictors of sarcopenia, with exercise type and duration as additional factors. These models illustrate the complex interplay of factors influencing sarcopenia risk and the potential for enhancing predictive capabilities in healthy populations while addressing confounders.
TABLE 4.
Adjusted model for mental health and physical activity indicators with the odds of sarcopenia by EWGSOP2 (2018).
| Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 | |
|---|---|---|---|---|---|---|
| Current physical activity | < 0.001 | — | < 0.001 | < 0.001 | < 0.001 | < 0.001 |
| Past physical activity | 0.225 | — | < 0.001 | < 0.001 | < 0.001 | 0.298 |
| Currently working | 0.118 | 0.003 | 0.004 | — | 0.009 | 0.031 |
| Kind of Work | 0.002 | < 0.001 | < 0.001 | — | < 0.001 | < 0.001 |
| Exercises activity | 0.815 | 0.003 | — | < 0.001 | 0.228 | 0.005 |
| Kind of exercises | 0.042 | 0.038 | — | < 0.001 | 0.421 | 0.025 |
| Depression (BDI‐13 score) | < 0.001 | < 0.001 | < 0.001 | < 0.001 | < 0.001 | — |
| Depression score (DASS‐21 score) | < 0.001 | < 0.001 | < 0.001 | < 0.001 | < 0.001 | — |
| Anxiety score (DASS‐21 score) | < 0.001 | < 0.001 | < 0.001 | < 0.001 | < 0.001 | — |
| Stress score (DASS‐21 score) | 0.008 | < 0.001 | < 0.001 | < 0.001 | < 0.001 | — |
Note: Frequency of population in each cell percentile according to the outcome (Sarcopenia) of the population.
Effect estimates with a p‐value < 0.05 are indicated in bold.
Model 1 adjusted for age and sex.
Model 2 adjusted for current physical Activity, past physical Activity, and physical activity change in time.
Model 3 adjusted for duration of exercise, kind of exercise, and exercise activity.
Model 4 adjusted for working and kind of work.
Model 5 adjusted for duration of exercise.
Model 6 adjusted for mental factors (Depression (BDI‐13 score), depression score (DASS‐21 score), anxiety score (DASS‐21 score), and stress score (DASS‐21 score)).
FIGURE 1.
This figure shows the predictive adjusted decision tree of having sarcopenia based on the physical activity level in the past, its change over time, having exercise activity and kind, and working status with its type. Decision trees were analyzed using the Chi‐Square Automatic Interaction Detector (CHAID) method. The decision tree suggested that physical activity change should be considered as the first node of predictors. Within people with decreased levels of physical activity, having exercise, and within people with the same level of physical activity change, past physical activity and currently working came at the next nodes.
FIGURE 2.
Bayesian network analysis showing the nodes and direct and indirect associations of main predictors with sarcopenia. The model included occupation and its kind, having sport and its kind, psychological factors including depression, anxiety, and stress assessed by DASS‐21 and BDI‐13, and physical activity history. Variables directly connected to the sarcopenia node indicate stronger conditional associations within the network. The direction of the links illustrates the dependency structure among variables and the potential pathways through which factors are associated with sarcopenia.
4. Discussion
This study suggests significant insights into the relationship between sarcopenia, physical activity, and mental health. It is among the first to examine the history of physical activity and occupations in a healthy, community‐based context, making it a valuable reference for future studies. The findings suggest that continued employment, higher levels of physical activity, regular exercise (in terms of both duration and type), and having better mental health are correlated with a reduced odds of sarcopenia.
A robust inverse relationship was identified between current physical activity and odds of sarcopenia, affirming existing literature that emphasizes physical activity's protective role against sarcopenia [1, 2, 8, 9, 11, 12, 26, 27]. Notably, while many studies focused predominantly on current activity, this research also spotlighted the significance of previous physical activity levels. The independent association of current activity with sarcopenia implies that regular physical engagement in older age may related to the sarcopenia.
The current study showed that past physical activity also emerged as a significant predictor of sarcopenia risk. When stripping away the influence of current activity, changes in physical activity over time showcased a more pronounced correlation with sarcopenia likelihood. Individuals with increased physical activity exhibited the lowest sarcopenia risk. Our findings suggest that considering past physical activity in relation to sarcopenia is important, as was demonstrated in previous study [28]. Although our understanding of long‐term physical activity's influence remains limited, evidence suggests that increased activity may enhance physical functioning in individuals with sarcopenia [29]. However, the absence of a validated tool for assessing previous physical activity poses a challenge. Hence, there exists a pressing need to develop appropriate assessment tools for evaluating past physical activity to minimize bias in future sarcopenia studies.
Occupational factors were notably linked to odds of sarcopenia; a reduced work level correlated with heightened sarcopenia likelihood, particularly among those with moderate activity levels who did not alter their activity patterns. This finding corroborates previous studies, suggesting working status contributes to the sarcopenia risk [30, 31]. Although some previous studies have reported a decrease in physical activity at work over time and that it can be influenced by some conditional such as pain [32, 33], the independence of the work‐related influence from physical activity suggests that occupational engagement itself may still be associated to sarcopenia risk.
This study underscores the role of occupation and exercise in sarcopenia risk. Tramontano et al. found that low physical activity levels contribute to an increased risk of sarcopenia [31]. Individuals over 55 with occupational histories in, military service, commercial services, and academic roles showed lower odds of sarcopenia compared to those with homeworker and individuals without work. Military personnel tend to show better health‐related outcomes, which is likely linked to their structured daily routines and consistent physical training [34, 35]. Conversely, healthcare professionals had higher odds of sarcopenia in comparison with reference group, potentially reflecting stressful working conditions, despite the statistical limitations of adjusting for stress effects among them [36].
Systematic reviews and meta‐analyses have documented improved muscle mass, strength, and performance in healthy individuals engaging in increased physical activity and exercise [9, 11, 12, 37]. Exercise can reduce sarcopenia risk independently, as repeated in the literature [1, 2, 8, 11, 12, 13, 26, 27, 37]. Liu et al. reported that incorporating aerobic and strength‐focused activities enhances muscle composition in at‐risk populations [29], supporting Scott et al.'s findings [38]. Nonetheless, the present study's exercise patterns diverged from these established correlations.
The present study suggests that swimming is beneficial in lowering the odds of sarcopenia. Heavy exercises, such as football and mountain climbing as well as, were also linked to absent sarcopenia cases. However, despite existing theories suggesting aerobic exercises may play a protective role against sarcopenia [13, 29, 38], no significant association was found between walking or aerobic activity and sarcopenia in the current study. This inconsistency may stem from differences in aerobic training protocols and engagement durations. Unlike controlled trial settings, community exercise variances might impact outcomes significantly. Notably, certain activities might not effectively enhance hand‐grip strength, which is an essential sarcopenia indicator [6], which could be explained by the method of swimming and high‐intensity versus walking and light aerobics sports, which consistently engage arm, hand, and foot muscles. However, exercise duration's effect on sarcopenia risk signifies the necessity of exploring exercise modality and frequency comprehensively [39]. It is suggested that engaging in 150–300 min per week of moderate‐intensity physical activity or 75–150 min per week of vigorous‐intensity physical activity can have health benefits [40]. It is another study that replacing sedentary time with as little as 10 min/day of moderate‐to‐vigorous physical activity may reduce the risk of sarcopenia [41]. Therefore, considering a daily routine physical activity plan for sarcopenia prevention based on guidelines can be beneficial.
Furthermore, the study explored the intersection of mental health, particularly depression, with sarcopenia. Depression manifests as a multifaceted issue with diverse risk factors [42, 43]. A higher prevalence of depression was reported in individuals with sarcopenia [44]. While Byeon et al. found no association between depressive symptoms and sarcopenia in a large Korean sample [18], another study by Kim et al. identified significant links between depression and sarcopenia in the Korean population [19]. Similarly, some other studies have supported the sarcopenia‐depression association, including findings from Ida et al. [45] in Japan and a Brazilian study due to low muscle strength [46]. Other research from Turkey, China, India, and Spain corroborates these results, enhancing the current study's findings [20, 47, 48, 49]. Also, meta‐analyses consistently affirm that sarcopenia is associated independently with depression across various conditions [7, 17]. However, the observed discrepancy between DASS‐21 and BDI‐13 measures in the current study's depression indicators can be attributed to sample size differences between the two tools.
Exploring anxiety and stress revealed that moderate anxiety correlated more strongly with sarcopenia than severe anxiety. However, individuals with higher anxiety, depression and stress scores were associated to increased odds of sarcopenia. Limited literature exists on stress and sarcopenia associations; yet, emerging evidence points to mental health as a contributing factor associated with sarcopenia [17, 48, 49, 50, 51]. The UK Biobank's findings reinforce this narrative, demonstrating inverse relationships between anxiety, depression, and handgrip strength [52].
This study's novel integration of current and past physical activity highlights the importance of addressing historical physical activity within sarcopenia research. The necessity for validating assessment tools is clear. Notably, routine physical activity through occupational engagement may offer more substantial protective benefits against sarcopenia than recreational sports. Future research should focus on longitudinal cohort studies to confirm these findings. The strengths of this study include its large, homogenized sample, minimizing intergroup variability [3]. However, limitations include the retrospective nature of the IPAQ in determining past physical activity and observational constraints impairing causal inferences. Future avenues should include prospective studies to elucidate causal dynamics between physical activity and sarcopenia and validate retrospective tools across demographic contexts, especially physical activity level. This exploration can deepen our understanding of how physical activity intersects with health dynamics.
5. Conclusions
This study reveals a significant link between mental health components such as stress, anxiety, and depression, physical activity, occupation, and sarcopenia in well‐nourished individuals aged 55 and older. Additionally, specific exercises, like swimming and high‐intensity sports, may contribute to lower odds of sarcopenia compared to the other types. While occupational engagement, as well as stress. anxiety, and depression as some of mental health components, and physical activity appears particularly related to the likelihood of sarcopenia, further research is essential to confirm these findings and assess their practical implications.
Author Contributions
Study concept and design: Zohre Hosseini, Mohammad Reza Shadmand Foumani Moghadam, Reza Rezvani, and Parnian Pezeshki. Drafting of the manuscript: Mohammad Reza Shadmand Foumani Moghadam, Zohre Hosseini, Reza Rezvani, Mostafa Shahraki Jazinaki. Study implementation: Mohammad Reza Shadmand Foumani Moghadam, Sharif Etemadi, Mohammad Amoushahi, Kazem Eslami and Reyhane Bakhshipour. Data validation: Mohammad Reza Shadmand Foumani Moghadam, Reza Rezvani, Zohre Hosseini, Sharif Etemadi, Majid Ghayour Mobarhan, and Mohammad Amoushahi Statistical analysis and interpretation of data: Majid Khadem‐Rezaiyan, Mohammad Reza Shadmand Foumani Moghadam. Managing the registry system: Sharif Etemadi. All authors gave final approval of the version to be published and agree to be accountable for all aspects of the work. Zohre Hosseini and Reza Rezvani also accept all responsibility for the manuscript on behalf of all the authors.
Funding
This work was supported by Mashhad University of Medical Sciences (4011283) and Varastegan Institute for Medical Sciences.
Ethics Statement
This research project is with approved code 4011283, which was discussed in the meeting of the University Research Council dated 12/10/1401 (Regional Ethics Committee in Medical science research dated 11/11/1401 and code IR.MUMS.MEDICAL.REC.1401.657). According to the Ethical Principles and Declaration of Helsinki, informed written consent was obtained individually from all participants.
Consent
The authors have nothing to report.
Conflicts of Interest
The authors declare no conflicts of interest.
Acknowledgments
Varastegan Institute for Medical Sciences supported this study. The authors want to thank Mr. Matin Etemadi, who managed the online registration programs. The authors acknowledge the Welfare Organization of Khorasan Razavi Province and Mr. Ramezani, the head of Khorasan Razavi Retirement Association, for supporting the study and all those who helped the authors in registration, gathering data, and performing this assessment as well as possible.
Contributor Information
Reza Rezvani, Email: rezvanir@mums.ac.ir, Email: reza.rezvani.rr@gmail.com.
Zohre Hosseini, Email: hosseiniz@varastegan.ac.ir, Email: hoseyni85.zh@gmail.com.
Data Availability Statement
The data that support the findings of this study are available on request from the corresponding or first authors. The data are not publicly available right now due to the privacy policy of the funder. However, data will be available as free‐access data in the future, which will be linked to the current manuscript.
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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 data that support the findings of this study are available on request from the corresponding or first authors. The data are not publicly available right now due to the privacy policy of the funder. However, data will be available as free‐access data in the future, which will be linked to the current manuscript.