Abstract
Background
Motoric Cognitive Risk Syndrome (MCR), a critical pre-dementia marker, synergistically predicts dementia and physical disability. While perceived physical fatigability (PPF) is prevalent in aging populations, its association with MCR and the mediating role of physical capabilities (balance ability, handgrip strength, IADL limitation) remain unexplored. This study pioneers a mechanistic investigation into how PPF contributes to MCR through declining physical function.
Methods
In a community-based cohort of 1,657 urban older adults (mean age = 82.5 ± 8.35 years), PPF was quantified using the validated Pittsburgh Fatigability Scale (PFS). MCR was diagnosed via standardized criteria. Multivariable logistic regression and Karlson-Holm-Breen mediation analysis dissected the PPF-MCR relationship, controlling for demographics, comorbidities, and lifestyle factors.
Results
Severe PPF (PFS ≥ 15) was associated with MCR risk (adjusted OR = 1.38, 95%CI:1.16–1.64). Mediation analysis revealed balance ability as the predominant pathway, explaining 33.33% of PPF’s effect on MCR (PM = 33.33%, p < 0.001), surpassing the non-significant mediation effects of handgrip strength (PM = 7.95%) and IADL limitation (PM = 0.7%).
Conclusion
This first-in-field study identifies PPF as a modifiable MCR risk factor, with decline in balance ability being the central mediator. Targeted interventions, such as balance training, could disrupt the PPF-MCR cascade by addressing this key pathway, offering novel strategies for dementia prevention in aging populations.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12877-026-07018-4.
Keywords: Mediating pathways, Physical capabilities, Perceived physical fatigability, Motoric cognitive risk syndrome, Older adults
Introduction
The motoric cognitive risk (MCR) syndrome is a pre-dementia condition characterized by an increased risk of Alzheimer’s disease (AD) and vascular dementia, as well as falls, disability, and abnormal movements [1]. This syndrome specifically refers to the presence of slowed gait speed and subjective cognitive complaints (SCC) in individuals who remain functionally independent and do not have dementia [2]. MCR represents a composite risk of both cognitive and physical decline, and its detrimental effects far exceed those of isolated cognitive or physical impairment. By combining two key indicators—slow gait and SCC—MCR provides an important window for early screening of pre-dementia syndromes. Compared to cognitive or physical decline alone, the presence of MCR not only indicates a higher risk of dementia but also suggests greater challenges in daily life activities.
Although extensive research has been conducted on predictors of MCR, the role of perceived fatigability and its mechanistic pathways through physical function remain unexplored. Our study uniquely addresses this gap by integrating patient-reported fatigability with objective measures of physical capability.
The incidence of MCR is significantly correlated with age [3, 4]. As such, MCR represents a potential target for preventing and intervening in pathological aging and its associated consequences. Identifying early signs that lead to MCR is essential for developing targeted interventions.
Perceived physical fatigability, defined as whole-body tiredness related to quantifiable activities or tasks of fixed intensity and duration, provides a sensitive patient-reported measure of the extent to which an individual is physically limited by fatigue [5–7]. It is highly prevalent among older adults, with reported rates ranging from 22.5% to 89.5% [8, 9]. Perceived physical fatigability increases with age, is more common in women than men, predicts declines in physical and cognitive function [10], and is associated with cardiovascular risk and depressive symptoms [11].
Perceived fatigability can be assessed using standardized tasks [12]. As a tool for evaluating physical health and function in older adults, it is widely applied in this population [10, 13]. Unlike traditional fatigue scales that focus on symptom frequency, the Pittsburgh Fatigability Scale (PFS) quantifies task-anchored fatigability, capturing functional limitations that are critical in aging research.
Physical function reflects an individual’s ability to perform daily life behaviors and often includes measures such as balance ability, grip strength, and the Timed Up and Go test. Recent studies suggest that declines in physical functions—such as slow gait speed and reduced grip strength—may serve as early predictors of motor cognitive risk syndrome or play a mediating role in disease progression [14, 15].
Research shows that motor decline in pre-dementia syndromes extends beyond gait speed to include impaired balance, a core component of lower extremity function. For instance, poor balance performance has been shown to predict future Alzheimer’s disease and is associated with its underlying neuropathology [16]. While previous studies attributed decreased grip strength to age-related muscle loss, emerging evidence suggests it may also be linked to impaired neurological function [17]. In the context of aging, maximum grip strength is considered a discriminating measure of neurological health and brain function [18, 19]. These findings highlight the need for further investigation into the relationship and interaction between physical ability, perceived physical fatigability, and motor cognitive risk syndrome in older adults.
Given the impact of fatigue on physical function, muscle activity, proprioception, and cognitive performance, fatigue is considered a potential early marker of dysfunction in both the body and the brain, signaling the onset and progression of motor cognitive disorders [20]. Low balance ability, weakened hand grip strength (HGS), and disability may mediate the effect of perceived fatigability on motor cognitive disorders. A deeper understanding of these associations can provide a foundation for designing effective interventions aimed at preserving brain health and enhancing independence and life satisfaction throughout the aging process.
We hypothesize that perceived physical fatigability (PPF) initiates a downward spiral of decline in physical function, which in turn increases the risk of MCR—a mechanistic model that has not been previously tested.
Methods
Study design and study population
A community-based cross-sectional study was conducted in Beijing city-dwellers during November 2023 -July 2024, which was the first wave of an ongoing longitudinal study. The study participants were recruited via community advertisements and would be eligible for the study when (1) aged over 60; and (2) voluntary participation with written inform consent. Participants would be excluded for reasons including presence with severe cognitive impairment, dementia, severe hearing loss, terminal cancer, or implanted cardiac pacemaker. The current study was ethically approved by the Research Ethics Committee of Chinese PLA General Hospital (Ethic number: S2022-087–04), with register in Chinese Clinical Trial Register (ChiCTR2300078965).All participants in the study signed the informed consent.
Perceived physical fatigability assessment
The Simplified Chinese version of the Pittsburgh Fatigability Scale (PFS) has been validated for use in multiple countries, including China, and is considered a sensitive tool for assessing perceived physical and mental fatigability among Chinese individuals [21, 22]. The PFS Physical Fatigability assesses the respondents’ self-rated physical tiredness under imagined scenarios of various activities, anchored by intensity and duration (ranging from 0 to 50, with higher scores indicating greater perceived physical fatigability). The binary method is used for scoring judgment, and a PFS body score of ≥ 15 is considered perceived physical fatigue [12, 23]. Accordingly, for the purposes of this study, participants with a PFS physical score < 15 were defined as having low perceived physical fatigability and constituted the reference group in subsequent analyses.
Motoric cognitive risk syndrome assessment
Motoric cognitive risk syndrome is defined as a condition characterized by slowness of gait in the presence of subjective cognitive complaint in older adults without any form of dementia or mobility disability [24, 25]. Motoric Cognitive Risk syndrome (MCR) is a clinical construct characterized by the coexistence of subjective cognitive complaints and objective slow gait in older adults, and it is recognized as a pre-dementia syndrome.
In previous studies, the diagnostic criteria for MCR were not uniform. This study is conducted in a community setting, so we define the diagnostic criteria as follows, referring to similar types of research [26].
The following four criteria have been proposed to be met for the diagnosis of MCR [27]: (1) presence of subjective cognitive complaints, assessed using standardized questionnaire (GDS-15 questionnaire), (2) presence of slow gait: defined as velocity one SD or more below age- and sex-appropriate mean values, (3) preserved mobility, and (4) absence of dementia.
Mediators assessment
Handgrip strength (HGS, in kilograms) was measured using the Jamar Plus + digital hand dynamometer (Sammons Preston, Bolingbrook, IL, USA), and a maximum of the two measurements (kg) from the dominant hand was recorded.
Balance was assessed using the Berg Balance Scale (BBS), a validated 14-item tool scored from 0 to 56. Higher scores indicate better balance function [28]. The BBS effectively evaluates both static and dynamic balance in older populations.
The evaluation of disability was conducted using the Lawton Index of Instrumental Activities of Daily Living (IADL) through questionnaires. Respondents were diagnosed with a disability if they reported any difficulty in managing money, taking medication, shopping, preparing meals, or performing housework. This assessment was treated as a dichotomous variable [29].
Covariates
Sociodemographic data, including age, gender, educational attainment, marital status, care situation, drinking, and smoking, were collected using structured questionnaires. Body mass index (BMI) was objectively measured using standardized protocols with a stadiometer and weighing scale. Clinical characteristics consisted of physician-diagnosed or self-reported history of hypertension, asthma, diabetes, and knee osteoarthritis. Depression is measured using the Geriatric Depression Scale-15 (GDS-15), with participants scoring above 2 points considered to have depressive symptoms [30]. GAD-7 is used to monitor the anxiety status of elderly people. According to the scoring criteria of the scale, patients (GAD-7 ≥ 10) are defined as anxiety status [31].
Sleep quality scores are calculated based on the Chinese version of the Pittsburgh Sleep Quality Index (PSQI), which is a self-reported questionnaire for assessing sleep quality [32].
Statistical analysis
Perform statistical analysis using Stata (version 17). Describe the basic characteristics of participants, presenting continuous variables that follow a normal distribution as mean and standard deviation, and categorical variable data as numbers and percentages. According to the type of data, use one-way ANOVA or chi square test to analyze the differences between the physical fatigue group and the normal group.
Fit a multivariate model based on adjustment factors: Model 1 is the unadjusted baseline model. In Model 2, we controlled BMI as a physical indicator; In model 3, we further incorporated marital status as a social factor. In model 4, we included basic health conditions, including diabetes, asthma, knee osteoarthritis, anxiety and depression symptoms. In subsequent models, we added balance ability, HGS and IADL limitation as covariates separately to evaluate the direct impact of these potential mediating variables on the occurrence of MCR. All models were adjusted for age, sex, BMI, educational attainment, marital status, care situation, smoking, drinking, hypertension, asthma, diabetes, knee osteoarthritis, depression, anxiety, and sleep quality (PSQI score).
To further investigate the mediating effects, we conducted mediation analysis using a logistic regression model based on the Karlson, Holm, and Breen (KHB) method framework. This method can decompose the total effect of variables into direct and indirect effects [33]. The Karlson-Holm-Breen method was prioritized over traditional mediation approaches due to its robustness in handling logistic models and covariate-adjusted effect decomposition. Subsequently, the mediation ratio (PM) was calculated to assess the impact of balance ability, HGS, and IADL limitation. In the above analysis, perceived physical fatigue level was used as a binary variable, normal (PFS < 15) or fatigue (PFS ≥ 15).
All analysis results are presented as 95% CI, x ± s, frequency, and percentage of ORs.A two-tailed P < 0.05 was considered statistically significant.
Results
Demographic characteristics
Among the 1,657 participants, 550 individuals (33.19%) exhibited symptoms of MCR, while 1,107 individuals (66.81%) did not show significant MCR. Perceived physical fatigue was observed in 624 cases (27.67%), and 1,033 individuals (62.34%) had not yet met the criteria for perceived physical fatigue. The average age of all participants was 82.52 ± 8.35 years, with 82.99 ± 8.32 years in the perceived physical fatigue group and 82.23 ± 8.35 years in the non-perceived erceived physical fatigue group. Compared to individuals in the low perceived physical fatigue group, those in the perceived physical fatigue group had a significantly higher BMI. There was no significant difference in gender ratio between the two groups (55.37% vs 55.77%, P = 0.875). The proportion of married individuals in the perceived physical fatigue was significantly lower than in the low perceived physical fatigue group (72.22% vs 67.63%, P = 0.038).In terms of clinical diseases, the incidence rates of diabetes, asthma, knee osteoarthritis, and adverse psychological states such as depression and anxiety among elderly individuals in the perceived physical fatigue group were significantly higher than those in the low perceived physical fatigue group. Consistent with the functional decline associated with fatigability, the perceived physical fatigue group also demonstrated significantly poorer performance on the Berg Balance Scale (BBS) compared to their low fatigability counterparts(P < 0.001) (Tables 1 and 2).
Table 1.
Participant Characteristics by Perceived physical fatigue (PFS Physical score ≥ 15) Versus Low perceived physical fatiguePerceived physical fatigue (PFS Physical Score < 15)
| Characteristics | All (N = 1657) |
Low perceived physical fatigue (n = 1033) |
Perceived physical fatigue (n = 624) |
P-value |
|---|---|---|---|---|
| Age, years | 82.52 ± 8.35 | 82.23 ± 8.35 | 82.99 ± 8.32 | 0.071 |
| BMI | 24.9 ± 2.94 | 24.26 ± 3.25 | 25.96 ± 1.92 | < 0.001 |
| Gender, n (%) | 0.875 | |||
| Male | 920 (55.5%) | 572 (55.4%) | 348 (55.8%) | |
| Female | 737 (44.5%) | 461 (44.6%) | 276 (44.2%) | |
| Educational Background, n (%) | 0.338 | |||
| College or above | 602 (36.3%) | 372 (36%) | 230 (36.9%) | |
| Junior college | 390 (23.5%) | 242 (23.4%) | 148 (23.7%) | |
| Secondary school or above | 322 (19.4%) | 208 (20.1%) | 114 (18.3%) | |
| Junior High school or below | 343 (20.7%) | 211 (20.4%) | 132 (21.2%) | |
| Marital Status, n (%) | 0.038 | |||
| Married | 1168 (70.5%) | 746 (72.2%) | 422 (67.6%) | |
| Widowed | 468 (28.2%) | 272 (26.3%) | 196 (31.4%) | |
| Divorce/Separation | 20 (1.2%) | 15 (1.5%) | 5 (0.8%) | |
| Unmarried | 1 (0.1%) | 0 (0%) | 1 (0.2%) | |
| Care situation, n (%) | 0.101 | |||
| With spouse | 680 (41%) | 448 (43.4%) | 232 (37.2%) | |
| With children | 405 (24.4%) | 241 (23.3%) | 164 (26.3%) | |
| With caregivers | 336 (20.3%) | 203 (19.7%) | 133 (21.3%) | |
| No one to care for | 236 (14.2%) | 141 (13.6%) | 95 (15.2%) | |
| Drink, n (%) | 544 (32.8%) | 324 (31.4%) | 220 (35.3%) | 0.102 |
| Smoke, n (%) | 442 (26.7%) | 273 (26.4%) | 169 (27.1%) | 0.770 |
All reported in mean ± SD or n (%)
Table 2.
Clinical diseases by Perceived physical fatigue (PFS Physical score ≥ 15) Versus Low perceived physical fatigue (PFS Physical Score < 15)
| Characteristics | All (N = 1657) |
Low perceived physical fatigue (n = 1033) |
Perceived physical fatigue (n = 624) |
P-value |
|---|---|---|---|---|
| Hypertension | 1162 (70.1%) | 715 (69.2%) | 447 (71.6%) | 0.297 |
| Asthma | 177 (10.7%) | 95 (9.2%) | 82 (13.1%) | 0.012 |
| Diabetes | 651 (39.3%) | 386 (37.4%) | 265 (42.5%) | 0.039 |
| Knee osteoarthritis | 573 (34.6%) | 332 (32.1%) | 241 (38.6%) | 0.007 |
| Depression | 313 (18.9%) | 142 (13.7%) | 171 (27.4%) | < 0.001 |
| Anxiety | 684 (41.3%) | 387 (37.5%) | 297 (47.6%) | < 0.001 |
| MCR | 550 (33.2%) | 305 (29.5%) | 245 (39.3%) | < 0.001 |
| PSQI | 10.13 ± 3.93 | 10.11 ± 3.94 | 10.15 ± 3.90 | 0.864 |
| MMSE | 24.81 ± 4.00 | 24.79 ± 4.02 | 24.84 ± 3.97 | 0.817 |
| IADL (≤ 7) | 628 (37.9%) | 359 (34.8%) | 269 (43.1%) | 0.001 |
| Handgrip strength (kg) | 22.1 ± 7.38 | 7.46 ± 0.23 | 7.21 ± 0.29 | 0.020 |
| Balance ability (BBS) | 48.17 ± 3.98 | 55.07 ± 3.10 | 45.01 ± 3.17 | < 0.001 |
All reported in mean ± SD or n (%)
Abbreviation: aIADL Instrumental Activity of Daily Living, bPSQI Pittsburgh Sleep Quality Index, cMMSE Minimum Mental State Examination
Association between perceived physical fatigability and MCR
Logistic regression (Table 3) shows that physical fatigue is associated with an increased risk of MCR, and ORs gradually decrease when covariates are controlled (from Model 1 to Model 4). After incorporating potential mediating factors into the models (Model 5–7), the correlation between perceived physical fatigue and MCR remained significant.
Table 3.
Associations between levels of perceived physical fatigability and MCR
| Models (MCR) | Odds ratio (95% Confidence Interval) | P-value |
|---|---|---|
| Model 1 (Unadjusted) | 1.543 (1.252,1.902) | < 0.001 |
| Model 2 (Model 1 plus BMI) | 1.525 (1.226,1.896) | < 0.001 |
| Model 3 (Model 2 plus Marital Status) | 1.512 (1.215,1.881) | < 0.001 |
| Model 4 (Model 3 plus Asthma and Diabetes and Knee osteoarthritis and depression and anxiety) | 1.470 (1.175,1.840) | 0.001 |
| Model 5 (Model 4 plus balance ability) | 1.361 (1.028,1.801) | 0.031 |
| Model 6 (Model 4 plus handgrip strength) | 1.439 (1.147,1.804) | 0.002 |
| Model 7 (Model 4 plus IADL limitation) | 1.466 (1.170,1.838) | 0.001 |
Mediation analysis
The mediation analysis revealed a statistically significant indirect effect through balance ability, which accounted for 33.33% of the total effect (Indirect effect: β = 0.150, 95% CI: 0.040, 0.260; p = 0.008). In contrast, the indirect effects through handgrip strength (PM = 7.95%, p = 0.061) and IADL limitation (PM = 0.70%, p = 0.834) were not statistically significant. Among the mediators examined, balance ability emerged as the only significant and substantial pathway. The mediation relationships between the variables are shown in Fig. 1 (Table 4).
Fig. 1.
Mediating path model of perceived physical fatigability and MCR
Table 4.
Mediation analyses of the association between levels of perceived physical fatigability and MCR
| Item | Total effect | P-value | Direct effect | P-value | Indirect effect | P-value | Mediated (%) |
|---|---|---|---|---|---|---|---|
| IADL limitation | 0.385 (0.161,0.610) | 0.001 | 0.383 (0.157,0.609) | 0.001 | 0.003 (−0.022,0.028) | 0.834 | 0.70 |
| Handgrip Strength | 0.040 (0.169,0.622) | 0.001 | 0.364 (0.137,0.590) | 0.002 | 0.031 (−0.001,0.064) | 0.061 | 7.95 |
| Balance Ability | 0.450 (0.210, 0.690) | < 0.001 | 0.300 (0.060, 0.540) | 0.015 | 0.150 (0.040, 0.260) | 0.008 | 33.33 |
Discussion
Motoric Cognitive Risk Syndrome (MCR) is a condition associated with cognitive decline, which is more common in older adults and has a certain correlation with physical fatigue. This study represents a pioneering effort in establishing the level of perceived physical fatigue as a reliable and independent indicator of Motoric Cognitive Risk Syndrome (MCR) in older adults.
Our study delivers three pivotal advances: (1) First evidence of PPF as an independent MCR predictor; (2) Quantification of balance ability’s dominant mediating role (33.33% effect); (3) Clinical validation of PFS as a scalable MCR risk screening tool. Our research indicate that individuals with severe perceived physical fatigue (PFS physical score ≥ 15) have a higher risk of MCR (OR = 2.79) compared to those with milder levels of fatigue. After adjusting for potential covariates such as sociodemographic characteristics, lifestyle habits, and underlying diseases, the correlation remains significantly present, with the odds ratio (OR) reduced by 8%. Moreover, we quantitatively assessed the mediating role of physical capabilities such as balance ability, handgrip strength and IADL limitation in explaining the association between perceived physical fatigability and MCR. Balance ability emerged as the only significant and substantial mediator, accounting for one third of the total effect, while the mediating effects of HGS and IADL limitation were not statistically significant.
Our study results are consistent with previous research, indicating that fatigue is an early indicator of adverse outcomes associated with cognitive decline and aging, significantly increasing the risk of functional decline [34] and all-cause mortality in the elderly [35]. Physical fatigue is significantly associated with the decline of physical and cognitive functions in older adults, and individuals with functional impairments are five times more likely to develop functional dependency in the future compared to those without such impairments [36]. This suggests that physical fatigue may lead to further cognitive decline by affecting the ability to perform daily activities. The particularly strong mediating role of balance ability found in our study underscores that the functional decline may be most pronounced in the domain of postural control and stability, which is fundamental to safe mobility.
Although studies generally agree that fatigue is closely associated with disability or various chronic diseases in the elderly population, the conclusions of prospective studies on the relationship between physical fatigue and cognitive function are not uniform. Research indicates that physical fatigue may have a negative impact on cognitive function, particularly during high-intensity or prolonged exercise, where this effect is more pronounced [37]. However, there is also research indicating that there is no significant association between fatigue and cognitive function. For instance, in patients with multiple sclerosis, despite reports of worse cognitive performance during periods of high fatigue, there were no significant differences in actual cognitive performance [38]. Additionally, studies on traumatic brain injury (TBI) and dementia have also found no significant association between fatigue and cognitive function [39]. Inconsistent findings across the literature can often be traced to the conflation of transient fatigue states with the more stable construct of perceived physical fatigability. The latter, measured as a trait-like vulnerability, is conceptually distinct from momentary tiredness and may show stronger and more consistent relationships with cognitive decline.
Emerging evidence also suggests that the pathological mechanisms underlying physical fatigue symptoms share similarities with those of Alzheimer’s disease, potentially being related to the accumulation of beta-amyloid (Aβ) protein [40]. Structural changes in the brain during the aging process, such as the reduction of gray matter and the enlargement of ventricles, can lead to cognitive dysfunction [41]. Physical fatigue may exacerbate these physiological changes, further affecting cognitive function [42].
Our study has confirmed the link between physical fatigue and MCR (Motoric Cognitive Risk) in an elderly community-dwelling population, which has significant implications for health policy and public health initiatives. The prevalence of MCR in this study is 33.1%, slightly higher than the previously reported range for community-dwelling older adults (6.5–18.42%) [43–45]. This discrepancy may be attributed to the higher average age in our sample (with an average age of 82.52 ± 8.35 years) and the inconsistency in the criteria used for assessment.
Our findings contribute to elucidating the mechanisms linking perceived physical fatigue with MCR and highlight the critical role of physical function in this association. Currently, there is no consensus on effective intervention strategies to alleviate fatigue, and there is an urgent need to understand the complex interplay of fatigue in the physiological, psychological, and social support aspects of older adults. Our study explored the relationship between fatigue and various indicators of physical and mental health as well as lifestyle habits. Previous research has confirmed that the higher the perceived level of physical fatigue, the lower the aerobic capacity (i.e., physical fitness) [46, 47], which will accelerates functional decline in older adults [6]. Furthermore, increased fatigue also leads to lower balance abilibty [48], which is a key predictive factor for MCR.
Our finding that balance ability is the primary mediator underscores its role as a marker of neurological integrity. Research shows that impaired balance, much like slow gait, is associated with Alzheimer’s disease pathology, including beta-amyloid deposition, and predicts future cognitive decline [49]. The deposition of beta-amyloid protein affects cognitive functions underpinning complex motor control, such as attention and executive function [50]. As age increases, the decline in the clearance of such proteins leads to accumulated damage in brain networks critical for both balance and cognition. This shared neuropathological burden may be a key mechanism underlying the strong association between balance ability and MCR observed in our study.
Research exploring the association between human genomics and cognitive function has yielded contradictory findings in terms of directionality. Some studies indicate that high handgrip strength (HGS) is associated with a reduced cognitive risk [51], while others suggest that impaired cognitive function is associated with low HGS [52]. While IADL disability is often a consequence of MCR, some longitudinal studies have also suggested that subtle IADL limitations can act as an early marker or risk factor for its development, highlighting the complex, bidirectional relationship between functional capacity and the syndrome [25]. This study demonstrates that IADL limitation has been shown to be a risk factor for Motoric Cognitive Risk Syndrome, increasing the risk of developing Motoric Cognitive Risk Syndrome. Future research needs to longitudinally assess the complex interplay between perceived physical fatigue, physical function, and MCR over an extended follow-up period to uncover potential mechanisms, ultimately protecting the health and quality of life of older adults.
Our mediation analysis identified balance ability as the predominant pathway, explaining approximately 33.33% of the association between perceived physical fatigability and MCR. This suggests that the experience of heightened fatigability may directly undermine an individual’s capacity to maintain postural stability, which in turn contributes to the development of the syndrome. The non-significant mediation effects of handgrip strength and IADL limitation suggest that the pathway from fatigability to MCR is more specifically related to the complex motor integration required for balance, rather than pure muscle strength or daily task performance. This finding aligns with the understanding that balance control is a highly integrative process involving sensory, motor, and cognitive systems, making it a sensitive marker for central nervous system integrity.
The strengths of this study include a considerably large cohort size, extensive case analysis, rigorous measurement of MCR (Motoric Cognitive Risk) and perceived physical fatigability, the inclusion of a robust objective measure of balance (Berg Balance Scale), and comprehensive availability of covariate data.
To our knowledge, no studies have thus far discussed the correlation between MCR (Motoric Cognitive Risk) and physical fatigue. The current analysis provides a unique perspective on exploring the relationship between perceived physical fatigue and Motoric Cognitive Risk Syndrome. Additionally, few studies have employed the PFS (Pittsburgh Fatigability Scale) to measure physical fatigue. The PFS is attractive due to its low cost, ease of administration, and practicality in both clinical and research settings. Unlike traditional fatigue assessment scales, the PFS physical fatigue enhances sensitivity and scope by standardizing fatigue assessment across activities in terms of intensity and duration [53]. The PFS is available in 20 language versions and has been culturally validated, promoting the integration of patient-centered measures in numerous research protocols worldwide [54].
Despite these findings, the study does have certain limitations. Firstly, the generalizability of our results should be tested with participants from a more diverse range of community populations, as the sample analyzed in this study is almost exclusively composed of older adults from the central urban areas of Beijing. Secondly, the cross-sectional nature of the study inherently includes residual confounding and the potential for reverse causality. Our findings should be replicated and extended to a longitudinal framework to assess the causal relationship between perceived physical fatigue and MCR. While we identify significant associations that align with our theoretical model, the inability to establish a clear temporal order is an inherent constraint. Thus, our results should be interpreted as revealing plausible biological or functional pathways that link perceived fatigability to MCR, rather than confirming causal mediation. These hypotheses now require rigorous testing in prospective cohort studies.
In conclusion, perceived physical fatigability (PPF) is an important and modifiable risk factor for Motoric Cognitive Risk Syndrome (MCR) in Chinese older adults. We propose a 'Fatigability-Balance Impairment-MCR' cascade model. Interventions combining balance training with resistance exercise could simultaneously target PPF and its primary mediating pathway. Given the predominant role of balance ability, strategies aimed at enhancing postural stability are likely to be particularly effective in interrupting the progression from perceived fatigability to MCR, offering a novel and targeted approach for dementia prevention in the aging population.
Supplementary Information
Acknowledgements
We acknowledge the staff and members of the research group studying cognitive function changes in middle-aged and elderly patients with chronic diseases at the PLA General Hospital, as well as the participants in the community survey. Special thanks to Dr. Luda for his work in data statistics. His outstanding contributions and the establishment of a causal mediating effect model have facilitated our exploration of influencing factors related to fatigue, weakness, and disability in elderly patients.
Abbreviations
- MCR
Motoric Cognitive Risk Syndrome
- PPF
Perceived physical fatigability
- PFS
Pittsburgh Fatigability Scale
- IADL
Instrumental Activity of Daily Living
- AD
Alzheimer’s disease
- SCC
Subjective cognitive complaints
- GS
Gait speed
- HGS
Handgrip strength
Authors’ contributions
Yixin Hu designed the study. Jing Zuo, Da Lu, Shaoni Liu, Xiaoyuan HY, Weiqiao Han analyzed and interpreted the data. Jing Zuo, Da Lu wrote the manuscript. Xiao Bai, Rongxia Chen, Liu Jian, Letian Wang, Cuilian LU, Zheng Liuparticipated in the collection of experimental data. All authors read and approved the final manuscript. Funding Not applicable.
Funding
This work was supported by Healthcare Fund (24BJZ26); Ningxia Autonomous Region’s Key R&D Program for Social Development (2024BEG02032).
Data availability
The data used to support the findings of this study are available from the corresponding author upon reasonable request.
Declarations
Ethics approval and consent to participate
The study was performed in conformity with the ethical principles of the Declaration of Helsinki. Ethical approval was obtained from the Research Ethics Committee of Chinese PLA General Hospital (Ethic Number: S2022-087–04), and written informed consent was obtained from all participants prior to their enrollment.
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.
Jing Zuo and Da Lu are co-first authors.
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Data Availability Statement
The data used to support the findings of this study are available from the corresponding author upon reasonable request.