The MCR was evaluated according to the criteria proposed in the literature (5, 26). MCR was identified if the following four criteria were met: (1) Subjective cognitive complaints (SCC): In this study, “Have you experienced a decline in your memory lately?” was used to assess SCC. Those who answered “Yes” were judged to have SCC. (2) Slow walking speed: The diagnostic criterion for sarcopenia recommended by the European Working Group on Sarcopenia in the older adults (EWGSOP2) (27) was used in this study. Gait speed was assessed using the 4-m gait speed assay. A total of 2 measurements were performed, and the average value was taken. Gait speed ≤0.8 m/s was defined as decreased gait speed. (The study population exhibited comparatively reduced mean gait speed. Since one standard deviation (SD) below this mean corresponded to approximately 0.8 m/s, this threshold was selected as the slow gait criterion based on the empirical distribution). (3) There was no movement disorder, and the ability to perform activities of daily living was preserved. The ADL score was less than 22. (4) Free of dementia: absence of dementia diagnosis in physical examination reports (where available, these reports took diagnostic precedence); Clinical Dementia Rating (CDR) score of 0 or 0.5; Mini-Mental State Examination (MMSE) scores exceeding established education-adjusted thresholds: >24 for junior high school education or above, >20 for primary school education, and >17 for illiterate individuals.

Data collection was conducted by multiple researchers who had undergone uniform training. They introduced the purpose of the study to the older population using a unified set of instructions in a one-on-one survey format. After informed consent was obtained from the older population, the survey was carried out, and a unified method and equipment were used to measure the relevant indicators. After the survey, the questionnaires were collected on the spot and checked for accuracy before being archived. Questionnaires with incomplete or inconsistent information were considered invalid. The study subjects general information and laboratory indicators were collected by reviewing electronic health examination reports.

SPSS 26.0 statistical software was used for data processing. Participants were divided into 2 groups according to the MCR criteria. MCR group and healthy control group. Normally distributed measurement data are expressed as x ¯  ± s. T-test was used for comparisons between groups. Count data are expressed as relative numbers. The chi-square test was used for comparisons between groups. The factors influencing MCR were analyzed using linear logistic regression and multivariate logistic regression analyses. p-value of <0.05 was considered to indicate statistical significance.

This study conducted a questionnaire survey among 5,389 rural older adults. Ultimately, 3,678 rural older adults who met the inclusion criteria were included. Of these, 429 rural older adults were diagnosed with MCR. The screening procedure is detailed in Figure 1.

A total of 3,678 rural older adults were included. The ages ranged from 60 to 90 years, with an average age of 69.4 ± 6.12 years. In terms of sex, 1,535 (41.7%) were males, and 2,143 (58.2%) were females. Most participants were married (81%) and lived with their spouses (78%). The number of children was 2 (53.6%) or 3 or more (36.9%). In terms of education level, most of them were illiterate (45.6%) and had a primary school education (28.7%). The proportion of those with a monthly income < 1,000 yuan (70.6%) was significant. There were 429 rural older adults with MCR, and the prevalence rate was 11.66% (see Table 1 for details).

The clinical data of the healthy control and the MCR groups were compared. There was no significant difference in the prevalence of MCR in rural older population based on sex, obesity status, sitting time or SASE score (p > 0.05). Other characteristics significantly affected the prevalence of MCR in rural older population (p < 0.05) (see Tables 1, 2 for details).

Multivariate and linear logistic regression were used to analyze the influencing factors of MCR among the rural older population, with the occurrence of MCR as the dependent variable (assignment: occurrence = 1, non-occurrence = 2). The variables with statistically significant differences in Tables 1, 2 were used as independent variables to conduct linear and multivariate logistic regression analyses. The assignments for the multifactor analysis variables are detailed in Table 3. The results of the analysis are detailed in Table 4.

In this study, 3,678 rural older population were analyzed, with 429 having MCR. The prevalence rate was 11.66%. Compared with other regions within China, the prevalence of MCR was higher than in Beijing (9.6%) and western China (10.7%) but lower than in eastern China (12.7%) (28). When compared with international studies, the prevalence rates in developing countries (3.5–15%) are higher than those reported in the United States (5.7–10.35%) (28, 29) and European countries (2.56–9.92%) (30). Compared to Germany (25.3%) (31) and Mexico (14.74%) (23), the detection rates in this survey were low. The lowest incidence is in Malaysia (3.5%) (16), and the highest is in India (15%) (32). This may be attributed to variations in sampling methods, study types, assessment and grading techniques, sample size, selection of survey instruments, definitions of study subjects, and the economic, educational, cultural, and demographic characteristics across different countries.

In the present study, older population aged 70–79 were more likely to suffer from MCR. However, the prevalence of MCR did not increase with age. The results of a longitudinal study on health and aging in Mexico (48) revealed that the incidence of MCR increased exponentially with age. This finding is not consistent with the results of this study. Previous studies (15, 33) have shown that advanced age is a critical risk factor for MCR. Age acts as a catalyst in the pathogenesis of dementia through the MCR pathway. This may be because the relationship between biological aging and MCR stems from a common biological mechanism (33). Increasing age, loss of sensory and motoric function, neurological and lifestyle changes, and reduced gait speed and cognitive ability contribute to the increased risk of MCR in older adults (16). Nevertheless, inconsistencies persist across studies regarding the relationship between age and Motoric Cognitive Risk (MCR) incidence, suggesting this association merits further investigation. Notably, the present study included a limited cohort of octogenarians (aged ≥80 years), which may constrain the generalizability of our findings to this high-risk demographic and partially account for the observed heterogeneity in age-related correlations.

Older population with 1–4 types of medication were more likely to suffer from MCR. A large-sample cross-sectional study in the American community (34) reported that the incidence of MCR was 10% in people with polypharmacy (5 or more types of medication), and the incidence of MCR was higher in people with polypharmacy. However, this finding is not consistent with the results of this study. Studies have shown that polypharmacy is a risk factor for MCR and is associated with poorer physical and cognitive function in older adults (24). This may be due to the adverse health outcomes of brain metabolism, brain structure, gait and cognition caused by the use of multiple drugs in older population (34). However, whether more kinds of medicines used results in a higher likelihood of MCR remains to be further studied.

Older adults with chronic diseases are more likely to suffer from MCR. Previous studies (17) have shown that some chronic diseases (such as diabetes, stroke, heart disease, chronic obstructive pulmonary disease, and coronary artery disease) are associated with an increased risk of MCR in older adults, which is consistent with the results of this study. The pathological basis of MCR is related to white matter hyperintensities, frontal lacunar infarcts, and gray matter atrophy in the premotor and prefrontal cortex. These pathologies are related to chronic diseases, especially cardiovascular diseases. A variety of chronic diseases can exacerbate the pathological state (22).

Additionally, the inflammatory response is the pathological basis of MCR (22). Inflammatory markers adversely affect walking speed and cognitive impairment in older adults (18, 19, 35). However, older adults with chronic diseases are more likely to have a disorder of proinflammatory cytokines and are more likely to suffer from MCR. In addition, heart-related diseases can lead to regional cerebral hypoperfusion in the cognitive regulatory regions of the brain, resulting in destructive effects on cognition (20).

Older adults with depression are more likely to suffer from MCR. Some studies have shown that (11) depression is a risk factor for MCR, which is consistent with the results of this study. Studies have shown that social activity positively correlates with gait speed and physical function (36, 37). Older adults with depression may reduce their social activity level, which increases the risk of MCR. Additionally, the decrease in social networks caused by cognitive decline and slow gait speed in older patients with MCR has adverse effects on their mental health (38). Furthermore, the main symptom of MCR is SCC, which overlaps with symptoms related to depression (23). Therefore, depression may be closely related to the MCR diagnosis.

Older adults with insufficient social support are more likely to suffer from MCR. Studies have shown that (21) adequate social support can reduce the risk of MCR, which is consistent with the results of this study. This may be because tangible social support increases gray matter volume in brain regions and reinforces MCR-associated neural substrates, thereby reducing the risk of MCR (39). Older adults with vision loss are more likely to suffer from MCR. Studies have shown that a lack of physical activity affects the normalization of blood glucose levels and memory function (22). An older population with vision loss may reduce physical activity to avoid injury, which affects cognitive function.

Older adults with a severe fear of falling are more likely to suffer from MCR. Studies have shown that decreased balance function, slow gait speed, and negative emotions can induce and aggravate the fear of falling (40). The cognitive motor risk composite is characterized by slow gait and subjective cognitive decline (41), which can lead to negative emotions (34). Therefore, older adults with cognitive motor risk syndrome are more likely to experience a fear of falling.

Older adults with chronic pain are more likely to suffer from MCR. Some studies (42) have shown that the severity of pain is closely related to the increased risk of MCR. This may be because chronic pain accelerates cognitive decline and slows gait speed in older adults (43). In addition, those living with children are more likely to suffer from MCR. No studies have addressed this point, but it may be related to overprotection. Older adults cared for by their children may experience decreased activities of daily living, memory and executive function.

Dementia burdens families, healthcare systems and societies (44). Currently, effective treatments for dementia are still lacking. Therefore, attention to predementia syndromes and their modifiable risk factors is critical. MCR is a predementia syndrome similar to mild cognitive impairment (MCI) (45), so future research should focus on MCR interventions. In rural areas, a support system can be established to increase social support for older adults living alone, providing them security and a sense of belonging. Future interventions could include physical activity (30), cognitive behavioral therapy (21), an anti-inflammatory diet (46), pain management (42), and medication management (24) in primary healthcare settings. These interventions would help strengthen their constitutions, reduce chronic pain, and improve mood and cognitive abilities. Professionals have attempted brain stimulation interventions (36), dual-task training (37), music interventions (47) and other approaches to improve memory problems and executive disorders in older adults. These interventions are beneficial for reducing the incidence of MCR and dementia. Few studies use professional instruments to measure related variables, and the data lacks objectivity. Future studies should focus on using professional tools to measure these variables, thereby improving the accuracy of the findings.

This study has several strengths. First, the large sample size improves the accuracy and reliability of the data analysis results. Second, few studies have investigated the influence of MCR in rural older populations in China. This study focuses on the rural older population, providing an in-depth understanding of this group’s incidence and related influencing factors of MCR. It should be noted that this study also has the following limitations. First, due to the cross-sectional design and lack of long-term follow-up, true causality cannot be determined. Second, the single-center nature of the study limits the generalisability of the findings. Third, the assessment tools are predominantly questionnaire scales, which introduces a degree of subjectivity. Fourth, Convenience sampling was used in this study, and there is a possibility of selection bias.