Factors associated with the risk of falls and their relationship with cognitive function in older adults with cognitive impairment: a cross-sectional study

Abstract

This study aimed to analyze the associations between cognitive function and factors linked to risk of falls in older adults with mild cognitive impairment (MCI). A cross-sectional design was applied in 129 older adults (mean age: 69.93 ± 3.84 years) diagnosed with MCI. Cognitive performance was evaluated with the MMSE, MoCA, Isaacs Test, Trail Making Test (TMT-A, TMT-B), D2 Test, and DSST. Risk of falls was assessed through the Tinetti Test, ABC-16 scale, and FES-I. Additional measures included gait speed, grip strength, and global physical function. Average MMSE and MoCA scores were 20.97 and 21.45, respectively. Regression models showed significant associations between cognitive and physical outcomes. Lower MMSE scores were related to weaker grip, slower gait, and greater risk of falls (R² = 0.053; p < 0.05). Lower MoCA scores was associated with reduced physical function, lower balance confidence, increased fear of falling, weaker grip, and higher risk of falls (R² = 0.107; p < 0.05). Isaacs performance was linked to grip strength, gait, balance, and fear of falling (R² = 0.126; p < 0.05). Both TMT-A and TMT-B correlated with grip, balance confidence, and risk of falls; TMT-A also with gait speed (R² = 0.059) and TMT-B with physical function (R² = 0.087). D2 scores were associated with function, gait, balance, and risk of falls (R² = 0.173 and 0.153). DSST performance correlated with all physical and psychological outcomes (R² = 0.133; p < 0.05). Cognitive performance is closely tied to physical and psychological factors associated with risk of falls in older adults with MCI. These findings emphasize the need for comprehensive, multidimensional assessments to identify risk profiles and develop targeted preventive interventions.

Introduction

Population ageing is one of the most important demographic phenomena of the twenty-first century, with significant implications for public health, the economy and social organization [1]. According to projections by the World Health Organization (WHO), the proportion of people aged 60 or over in the world will reach 22% of the global population in 2050, doubling their number since 2017 [2]. Spain is one of the European countries with the highest percentage of older adults: 19% of the population is aged 65 or over, and this Fig. is expected to exceed 25% in 2030 [3]. This phenomenon has important implications for the prevalence of chronic diseases and the need to implement public policies that improve the quality of life of this age group, particularly for those suffering from cognitive impairment [4].

Mild cognitive impairment (MCI) is a common condition among older adults and is considered an intermediate stage between normal aging and more severe forms of decline, such as dementia [5]. Although MCI does not severely affect people's daily activities, it is associated with an increased risk of progression to dementia [6]. In Spain, it is estimated that between 10 and 20% of people over 65 years of age have some type of cognitive impairment, with a prevalence that increases with age [7]. People with MCI experience impairments in several cognitive areas essential for daily life, such as memory, attention, verbal fluency, executive functions and processing speed, which can affect their autonomy and increase the risk of falls, one of the main health concerns in old age [8].

Cognitive impairment is a common condition in older adults and can affect various mental functions necessary for balance and mobility, including general cognitive performance, verbal fluency, executive functions, selective attention, concentration, and processing speed [9]. These functions allow for planning movements, adapting to unforeseen situations, and making quick decisions, all of which are essential skills for avoiding falls [10, 11]. Executive functions, for example, involve planning, inhibitory control, and cognitive flexibility, all of which are necessary for safely managing daily activities [12, 13]. Verbal fluency and selective attention—closely linked to executive functions—are also often affected, hampering effective communication and the ability to concentrate on specific tasks [14–17]. Decreased processing speed, meanwhile, compromises rapid decision-making when faced with obstacles or loss of balance, thus increasing the risk of falls [18].

In addition to cognitive factors, physical abilities are crucial in preventing falls in older adults. It is estimated that approximately 30% of people over 65 years of age suffer at least one fall per year, a Fig. that increases in the presence of cognitive impairment [19]. Grip strength, is essential for maintaining balance and recovering from losses of stability [20–22]. Likewise, gait speed and general physical function are key indicators of mobility and predictors of risk of falls, especially in people with MCI, who tend to have a slower gait and less coordination [23, 24].

Psychological factors such as confidence in balance and fear of falling also have a negative influence, as they can reduce physical activity, cause muscle weakness, and increase postural instability [25, 26]. Finally, balance and gait, affected by aging and cognitive impairment, are essential for preventing falls, particularly in situations that require rapid postural changes or complex movements [27–32].

Despite this interaction between cognitive impairment and physical function, most studies have addressed these factors in isolation. There is limited research that jointly analyzes how different cognitive domains—such as attention, executive functions, or processing speed—relate to key functional variables such as grip strength, gait, balance, or fear of falling, particularly in older adults with MCI. Furthermore, available studies typically focus on a limited number of tests, without integrating comprehensive cognitive batteries or standardized functional assessment tools [21, 22]. This lack of integrated evidence represents a significant gap in the literature, as it hinders the identification of complex risk profiles and the development of effective preventive strategies. Consequently, a multidimensional approach is required to more accurately understand the interaction between cognitive impairment and physical factors associated with the risk of falls. Understanding the associations between specific cognitive domains and the physical and psychological factors related to risk of falls in older adults with MCI is essential for developing more accurate and effective screening and intervention strategies. Identifying integrated risk profiles that include both cognitive and physical dimensions could support the design of personalized preventive programs, improve clinical decision-making, and contribute to reducing the functional, social, and economic burden associated with falls in this vulnerable population.

Therefore, the objective of this study is to analyze the associations between different cognitive domains (global cognitive function, cognitive impairment, verbal fluency, executive functions, selective attention, concentration, and processing speed) and various factors related to the risk of physical falls (grip strength, physical function, gait speed, balance confidence, fear of falling, balance, and gait) in older adults with mild cognitive impairment. It is hypothesized that better performance in cognitive and physical functions will be associated with a lower risk of falls, although these associations are expected to be modest due to the multifactorial nature of fall risk in older adults with mild cognitive impairment.

Materials and methods

Study design and participants

This analytical cross-sectional study involved 129 older adults diagnosed with mild cognitive impairment (Fig. 1), ranging in age from 60 to 78 years. The study was approved by the Ethics Committee of the University of the Middle Atlantic (CEI05-012) and was carried out in compliance with the principles outlined in the Declaration of Helsinki, as well as good clinical practice guidelines and relevant laws. Prior to participation, all subjects provided written informed consent. The participants had to meet the following inclusion criteria to be part of the study: (i) be men or women aged 60 years or older; (ii) score less than 24 on the Mini-Mental State Examination (MMSE); (iii) have the ability to understand instructions, answer questionnaires designed for the research, and participate in the required physical tests; (iv) sign an informed consent. On the other hand, older adults were excluded if: (i) they suffered from vestibular disorders or diseases; (ii) they were being treated with medications that affect the central nervous system, balance, or coordination, such as antidepressants, anxiolytics, vestibular sedatives, or drugs to improve cognition or vestibular function; (iii) they had visual problems that could not be corrected with glasses, contact lenses, or surgery. These conditions included progressive diabetic retinopathy, advanced glaucoma, advanced macular degeneration,,, and other eye conditions that severely affected central or peripheral vision; iv) they had a prior clinical diagnosis of dementia or scored below 18 points on the MMSE, a threshold used to differentiate mild cognitive impairment from moderate to severe cognitive decline. This cutoff ensured that participants met established criteria for MCI and did not present significant functional impairment characteristic of dementia (Fig. 1).

Fig. 1.

Flowchart of the study design

Outcomes

Demographic information, such as age, education level, marital status, and employment status, was gathered by trained interviewers. Body mass index (BMI) was calculated by dividing weight in kilograms by height in meters squared (kg/m²). A highly accurate digital scale (100 g-130 kg, Tefal, Barcelona, Spain) and an adult stadiometer (Asimed, T201-T4, Barcelona, Spain) were used to measure weight and height. Waist circumference was measured using a 1.5 m flexible tape measure (Lufkin, W606PM, MD, USA), with the measurement taken at the midpoint between the lowest rib and the iliac crest, while the participants were standing. In addition, each participant was asked, in a direct and structured manner, whether they had suffered one or more falls in the past month. The response was recorded as a dichotomous variable (yes/no).

Cognitive function

The Mini-Mental State Examination (MMSE) was used to evaluate global cognitive function, a widely recognized tool for identifying significant cognitive impairments [33]. This test assesses five key cognitive areas: attention, calculation, orientation, memory, and language. The maximum possible score is 30, representing optimal cognitive function. Reference values are classified as follows: scores of 27 or higher are considered normal, while scores of 24 or above may suggest potential cognitive issues. Scores between 12 and 24 indicate cognitive impairment, and scores ranging from 9 to 12 are linked to symptoms of dementia. In the present study, MMSE scores ranged from 19 to 23 points, indicating that all participants were within the mild cognitive impairment range, according to the inclusion criteria.

Cognitive impairment

The Montreal Cognitive Assessment (MoCA) is a brief assessment comprising 12 tasks aimed at evaluating seven essential cognitive functions: visuospatial and executive abilities (including tasks like copying a cube, tracing a Fig., and drawing a clock), object naming, attention, working memory (such as recalling sequences of numbers in reverse), sustained attention (measured through a task requiring consistent responses), language proficiency (including sentence repetition and verbal fluency), abstract reasoning (assessed through verbal tasks), as well as short- and long-term memory and temporal and spatial orientation. The maximum score is 30 points, with a score of 26 or higher indicating cognitive function within the normal range [34]. MoCA scores ranged from 19 to 25 points in this sample, reflecting moderate variability in cognitive performance within the mild impairment spectrum.

Verbal fluency

Verbal fluency was evaluated using the Isaac test, where participants were asked to list as many words as possible from a given semantic category (such as animals, fruits, cities, or colors) within 60 s. Each category had a maximum score of 10 points, for a total possible score of 40 points. A higher score indicated a greater level of verbal fluency [35]. In our sample, Isaacs test scores ranged from 14 to 20 out of a possible 40 points, indicating mild to moderate limitations in semantic fluency.

Executive functions

Executive function was assessed using the Trail Making Test (TMT), which focuses on tasks requiring motor coordination and visual abilities under time constraints. The test is divided into two sections: TMT-A, which measures attention and speed by having participants connect numbered circles in sequence, and TMT-B, which involves connecting alternating numbers and letters, specifically evaluating executive function [36]. In this context, taking a longer time to complete the task was considered an indication of lower performance efficiency. TMT-A times ranged from 64 to 99 s, while TMT-B ranged from 38 to 300 s, showing greater dispersion in executive functioning performance.

Selective attention and concentration

The Spanish version of the d2 Test of Attention [37] is designed to evaluate selective attention and concentration, commonly used in educational environments. In this test, participants are instructed to carefully scan each line from left to right and cross out every occurrence of the letter "d" that is surrounded by two small lines (either two lines above, two lines below, or one above and one below). These marked instances are considered relevant, while other combinations, such as "d" or "p" with different line patterns or no lines, are regarded as "distractors" and should not be marked. Each line must be completed within 20 s. This test has shown high convergent and divergent validity, along with excellent reliability, with Cronbach's alpha and test–retest reliability ranging from 0.90 to 0.97 [38]. In the d2 test, concentration scores varied between 40 and 66 points, while selective attention scores ranged from 128 to 145 points, indicating fairly stable attention levels.

Processing speed

The Digit Symbol Substitution Test (DSST) [39] is a paper-based cognitive assessment in which participants match symbols to numbers based on a key provided at the top of the page. The task requires the participant to copy the appropriate symbol into the spaces corresponding to a sequence of numbers. The score is calculated by counting the number of correct symbol substitutions made within 90 s. DSST scores in this sample ranged from 35 to 50 items completed in 90 s, showing moderate variability in processing speed.

Grip strength

Grip strength (kg) was assessed using an analog hand dynamometer (TKK 5001, Grip-A, Takei, Tokyo, Japan), following a standardized protocol previously described in the literature [40]. Participants were assessed in a seated position, with the shoulder adducted and neutrally rotated, the elbow flexed at 90°, the forearm in a neutral position, and the wrist in slight extension. Two maximal voluntary contractions were performed with the dominant hand, with a brief rest period between attempts, and the average of both measurements was used for analysis [41]. In the present study, grip strength values ranged from 12.40 to 38.80 kg, reflecting variability in muscle strength within the sample.

Physical performance

Physical performance was measured by gait speed using the Timed Up-and-Go (TUG) test [42]. In the present study, TUG test scores ranged from 11 to 27 s, reflecting variability in physical performance and mobility within the sample.

Gait speed

Gait speed was estimated from the time obtained in the Timed Up and Go (TUG) test using the formula: Speed ​​(m/s) = (6/TUG time) × 1.62, where 6 represents the total distance traveled (in meters) during the TUG. This formula has been previously used to adjust the speed derived from the TUG and make it comparable to that obtained through linear gait tests, compensating for the turning, sitting-to-standing transition, and turning phases [43, 44]. A low gait speed was considered a value ≤ 0.8 m/s, according to standardized criteria. In the present study, gait speed, estimated from the TUG test, ranged from 0.36 to 0.88 m/s, reflecting variability in walking performance within the sample. Low gait speed was considered when values ​​were ≤ 0.8 m/s [45].

Fall risk assessment

The risk of falls in participants was assessed using two validated instruments widely used in the geriatric setting: the Tinetti scale and the ABC-16 scale (Activities-specific Balance Confidence scale).

The ABC-16 scale was used to evaluate participants' confidence in maintaining balance while performing daily activities [46]. This 16-item questionnaire measures the level of confidence in completing specific tasks without losing balance or feeling unsteady [47]. Each item is scored on a scale from 0 to 100%, and the total score is calculated by summing the individual ratings (ranging from 0 to 1600) and dividing by 16. In the present study, ABC-16 scores ranged from 52 to 97, indicating variability in balance confidence among the participants. A higher percentage score reflects greater balance confidence. A score below 67% has been identified as a reliable predictor of future falls [48]. This threshold was used to identify participants at high risk of fall.

The Tinetti scale is used to assess postural stability, balance, and gait, allowing healthcare professionals to determine a patient's risk of falls [48–50]. The assessment consists of two sections: the first evaluates balance (maximum score of 16 points) and the second evaluates gait (maximum score of 12 points), yielding a total score of 28 points. Higher total scores indicate better balance and gait performance and, consequently, a lower risk of falls, whereas lower scores reflect poorer performance and a higher risk of falling. In the present study, balance scores ranged from 8 to 15 points and gait scores from 6 to 12 points. According to established criteria, total scores between 19 and 24 points indicate a low risk of falls, while scores below 19 points suggest a high risk. In this study, total Tinetti scores ranged from 14 to 26 points. The Tinetti scale was used in combination with the ABC-16 scale to characterize the risk of falls from both a functional (objective) and a subjective psychological perspective, allowing a multidimensional evaluation of fall risk.

Fear of falling

Fear of falling was measured using the Spanish version of the Falls Efficacy Scale-International (FES-I) [51]. This scale is composed of 16 items that assess concern about falling when performing basic activities of daily living. Participants rated each item on a scale ranging from 1 (no fear or concern about falling) to 4 (great fear or concern about falling). The total score ranges from 16 to 64 points, where a higher score reflects greater concern about falling.

Sample size calculation

Sample size and power were calculated using G*Power software (version 3.1.9.2) [52], with a 95% confidence interval, a precision of 6%, and an estimated incidence of falls of 30% in people over 60 years of age [53, 54]. Using these parameters, it was determined that the minimum number of participants required for a statistical power of 80% was 125 people. No additional adjustments for losses were necessary, since the sample obtained already met the power and precision requirements. Finally, the sample was randomized using a consolidated list of participants, to whom a number was assigned and, using a simple randomization process, 125 people were invited to participate in the study.

Data analysis

Continuous variables were presented as means and standard deviations, while categorical variables were reported as frequencies and percentages. The Kolmogorov–Smirnov test was employed to check for the normal distribution of variables. A bivariate correlation analysis was conducted to examine potential individual associations between independent variables such as grip strength, physical function, gait speed, balance confidence, fear of falling, balance, and gait, as well as other covariates including age, sex, BMI, falls, and smoking habits, in relation to cognitive function, cognitive impairment, verbal fluency, selective attention, concentration, executive functions, and processing speed. Additionally, scatter plots were generated to visually represent the relationships between these variables and to identify potential patterns of association. To further investigate independent associations between variables, a multivariate linear regression model with a stepwise method for variable inclusion was applied. Grip strength, physical performance, gait speed, balance confidence, fear of falling, balance, gait, and risk of falls were each entered separately as dependent variables in distinct models. Initially, bivariate correlation coefficients were analyzed, and variables with significant associations (p < 0.05) were included in the multivariate linear regression. The R² value was used to calculate the effect size coefficients for multiple determination in the linear models. R² values were interpreted as negligible if < 0.02, small if between 0.02 and 0.15, moderate if between 0.15 and 0.35, and large if > 0.35 [55]. A 95% confidence level was applied (p < 0.05). Data management and analysis were performed using the SPSS statistical software for Windows (SPSS Inc., Chicago, IL, USA).

Results

The characteristics of the participants are detailed in Table 1. In total, 129 subjects with a mean age of 69.93 ± 3.84 years were included, of whom 80 were women (62%) and 49 men (38%). 62.8% were married, 25.6% had completed primary or university education, while the majority (67.4%) were unemployed. The mean body mass index was 25.02 kg/m², which corresponds to a normal weight range. On the other hand, the results on the cognitive tests MMSE (20.97 ± 1.31) and MoCA (21.45 ± 1.68) indicated cognitive functioning within the parameters compatible with mild cognitive impairment.

Table 1.

Descriptive data of the participants (n = 129)

Characteristics		Values
		Mean	SD
Age (years)		69.93	3.84
Weight (kg)	Male	76.64	6.83
	Female	66.54	6.52
Height (m)	Male	1.74	0.57
	Female	1.64	0.62
BMI (kg/m2)		25.02	1.15
		Frequency	Percentage
Sex	Female (%)	49	38.00
	Female (%)	80	62.00
Marital status	Single (%)	26	20.20
	Married (%)	81	62.80
	Separated/divorced/Widowed (%)	22	17.10
Education status	No formal education (%)	31	24.00
	Primary education (%)	33	25.60
	Secondary education (%)	32	24.80
	University (%)	33	25.60
Occupational status	Retired (%)	42	32.60
	Unemployed (%)	87	67.40
Falls	Yes (%)	71	55.00
	No (%)	58	45.00
Smoker	Yes (%)	110	85.30
	No (%)	19	14.70
		Mean	SD
Cognitive function (MMSE)		20.97	1.31
Cognitive impairment (MoCA)		21.45	1.68
Verbal fluency (Isaac test)		17.19	1.73
Attention and speed (TMT-A)		80.53	9.64
Executive functions (TMT-B)		169.72	65.94
Concentration (d2)		58.67	5.30
Selective attention (d2)		138.30	4.30
Processing speed (DSST)		43.60	3.50
Grip strength (dynamometry)		24.84	5.00
Physical Performance (TUG test)		18.73	3.36
Gait speed (TUG test score formula)		0.54	0.10
Balance confidence (ABC-16)		72.13	10.88
Fear of falling (FES-I)		35.94	10.02
Balance (Balance _ Tinetti)		10.94	1.92
Gait (Gait_Tinetti)		9.43	1.76
Risk of falls (Total Score Tinetti)		20.37	2.66

BMI body mass index, MMSE the mini-mental state examination, MoCA the montreal cognitive assessment, TMT Trail Making Test, DSST the digit symbol substitution test, TUG the timed up-and-go test, ABC-16 The activities-specific balance confidence, FES-I  the falls efficacy scale-international

Analysis of individual associations revealed that lower strength (r = 0.182, p = 0.039), lower gait speed (r = −0.193, p = 0.028), and lower gait performance (r = 0.176, p = 0.046), along with a higher risk of falls (r = 0.012, p = 0.012), were significantly related to lower MMSE scores (Fig. 2) (Table 2).

Fig. 2.

Associations between global cognitive function (MMSE) and physical and fall-related variables (grip strength, gait speed, balance, and risk of falls)

Table 2.

Pearson correlations between cognitive outcomes and factors related to risk of falls and and possible confounders

Variables		Grip strength	Physical Performance (TUG Test)	Gait speed (TUG test score formula)	Balance confidence (ABC-16)	Fear of falling (FES-I)	Balance (Balance _ Tinetti)	Gait (Gait _ Tinetti)	Risk of falls (Total Score Tinetti)
Cognitive function (MMSE)	r	0.182*	0.170	−0.193*	0.156	0.148	0.142	0.176*	0.220*
	p-value	0.039	0.054	0.028	0.078	0.095	0.107	0.046	0.012
Cognitive impairment (MoCA)	r	0.208*	0.180*	−0.211*	0.171	0.217*	0.171	0.176*	0.240**
	p-value	0.018	0.042	0.016	0.053	0.014	0.053	0.046	0.006
Verbal fluency (Isaac test)	r	0.171	0.230**	−0.294**	0.154	0.202*	0.222*	0.197*	0.292**
	p-value	0.053	0.009	0.001	0.082	0.022	0.012	0.025	0.001
Attention and speed (TMT-A)	r	0.184*	0.173*	−0.180*	0.178*	0.139	0.142	0.181*	0.223*
	p-value	0.037	0.050	0.041	0.043	0.116	0.107	0.040	0.011
Executive function (TMT-B)	r	0.185*	0.178*	−0.202*	0.174*	0.154	0.169	0.173	0.237**
	p-value	0.036	0.044	0.022	0.049	0.081	0.055	0.050	0.007
Concentration (d2)	r	0.150	0.282**	−0.375**	0.221*	0.209*	0.245**	0.210*	0.317**
	p-value	0.090	0.001	0.000	0.012	0.017	0.005	0.017	0.000
Selective attention (d2)	r	0.194*	0.223*	−0.273**	0.194*	0.203*	0.209*	0.199*	0.284**
	p-value	0.028	0.011	0.002	0.028	0.021	0.017	0.024	0.001
Processing speed (DSST)	r	0.196*	0.216*	−0.261**	0.200*	0.183*	0.197*	0.178*	0.261**
	p-value	0.026	0.014	0.003	0.023	0.038	0.025	0.044	0.003
Age	r	−0.046	−0.117	0.146	−0.046	−0.305	−0.081	−0.071	−0.106
	p-value	0.602	0.187	0.100	0.608	0.000	0.361	0.427	0.233
Sex	r	−0.038	0.036	0.019	0.046	−0.105	0.084	0.098	0.125
	p-value	0.668	0.689	0.830	0.602	0.238	0.346	0.271	0.157
BMI	r	0.108	0.048	−0.041	0.011	0.178*	−0.004	0.032	0.019
	p-value	0.224	0.587	0.648	0.902	0.044	0.964	0.716	0.834
Falls	r	0.041	−0.105	0.054	0.089	−0.102	0.086	−0.019	0.050
	p-value	0.645	0.236	0.542	0.314	0.248	0.331	0.828	0.577
Smoker	r	−0.048	−0.060	0.065	−0.017	−0.012	0.082	−0.040	0.033
	p-value	0.588	0.501	0.466	0.848	0.892	0.356	0.649	0.715

BMI body mass index, * p < 0.05. ** p < 0.01, MMSE the mini-mental state examination, MoCA the montreal cognitive assessment, TMT trail making test, DSST the digit symbol substitution test, TUG the timed up-and-go test, ABC-16 the activities-specific balance confidence, FES-I the falls efficacy scale-international

Similarly, lower values ​​in strength (r = 0.208, p = 0.018), physical performance (r = 0.180, p = 0.042), gait speed (r = −0.211, p = 0.016) and gait (r = 0.176, p = 0.046) (Fig. 3) (Table 2).

Fig. 3.

Associations between cognitive impairment (MoCA) and physical performance and gait-related variables

Furthermore, it was associated with greater fear of falling (r = 0.217, p = 0.014) and higher risk of falls (r = 0.240, p = 0.006), were related to lower MoCA scores (Fig. 4) (Table 2).

Fig. 4.

Associations between cognitive impairment (MoCA) and psychological and functional fall-related variables (fear of falling, balance, and risk of falls)

Lower physical performance (r = 0.230, p = 0.009), lower gait speed (r = −0.294, p = 0.001), and higher fear of falling (r = 0.202, p = 0.022) were associated with lower Isaac test scores (Fig. 5) (Table 2).

Fig. 5.

Associations between verbal fluency (Isaacs Test) and physical performance and gait speed

Furthermore, it was associated with reduced balance (p = 0.012), lower gait speed (p = 0.025), higher risk of falls (r = 0.292, p = 0.001), were associated with lower Isaac test scores (Fig. 6) (Table 2).

Fig. 6.

Associations between verbal fluency (Isaacs Test) and balance, gait, and risk of falls

Lower gait strength (r = 0.184, p = 0.037), grip speed (r = 0.292, p = −0.180), balance confidence (r = 0.178, p = 0.043), gait (r = 0.181, p = 0.040) and conversely, higher risk of falls (r = 0.223, p = 0.011) were associated with higher TMTA scores (Fig. 7) (Table 2).

Fig. 7.

Associations between attention and processing speed (TMT-A) and physical and psychological fall-related variables

Similarly, strength (r = 0.185, p = 0.036), physical performance (r = 0.178, p = 0.044), gait speed (r = −0.202, p = 0.022), balance confidence (r = 0.174, p = 0.049) and risk of falls (r = 0.237, p = 0.007) were related to lower TMTB scores (Fig. 8) (Table 2).

Fig. 8.

Associations between executive function (TMT-B) and physical performance, gait-related variables, and fall risk

The concentration domain of the d2 test was associated with physical performance (r = 0.282, p = 0.001), gait speed r = −0.375, p = 0.000), balance confidence (r = 0.221, p = 0.012) and fear of falling (r = 0.209, p = 0.017) (Fig. 9) (Table 2).

Fig. 9.

Associations between concentration (d2 Test) and physical performance, gait speed, and psychological fall-related variables

Furthermore, it was associated with balance (r = 0.245, p = 0.005), gait (r = 0.210, p = 0.017), and risk of falls (r = 0.317, p = 0.000) (Fig. 10) (Table 2).

Fig. 10.

Associations between concentration (d2 Test) and balance, gait, and fall risk

The total d2 test score was related to strength (r = 0.194, p = 0.028), physical function (r = 0.223, p = 0.011), gait speed (r = −0.273, p = 0.002) and balance confidence (r = 0.194, p = 0.028) (Fig. 11) (Table 2).

Fig. 11.

Associations between selective attention (d2 Test) and physical performance and balance confidence

Furthermore, it was associated with fear of falling (r = 0.203, p = 0.021), balance (r = 0.209, p = 0.017), gait (r = 0.199, p = 0.024), and risk of falls (r = 0.284, p = 0.001) (Fig. 12) (Table 2).

Fig. 12.

Associations between selective attention (d2 Test) and psychological and functional fall-related variables

Finally, a low DSST score was associated with lower scores on strength (r = 0.196, p = 0.026), physical performance (r = 0.216, p = 0.014), gait speed (r = −0.261, p = 0.003), balance confidence (r = 0.200, p = 0.023) (Fig. 13) (Table 2).

Fig. 13.

Associations between processing speed (DSST) and physical performance and balance confidence

Furthermore, it was associated with fear of falling (r = 0.183, p = 0.038), balance (r = 0.197, p = 0.025), gait (r = 0.178, p = 0.044), and risk of falls (r = 0.261, p = 0.003) (Table 2) (Fig. 14).

Fig. 14.

Associations between processing speed (DSST) and psychological and functional fall-related variables, including fall risk

Multivariate linear regression analysis (Table 3) identified several independent associations with the different risk factors for falls assessed in this study. Cognitive impairment was associated with decreased grip strength (R² = 0.043). Attention and speed, executive functions, and falls were associated with lower physical performance scores (R² = 0.078). Cognitive function, attention and speed, executive functions, and concentration were associated with reduced gait speed (R² = 0.256). Executive functions were associated with lower balance confidence (R² = 0.079). Cognitive impairment, concentration, and older age were associated with increased fear of falling (R² = 0.144). Lower concentration was associated with decreased balance scores (R² = 0.074). Cognitive function, verbal fluency, and selective attention were associated with decreased gait (R² = 0.198). Cognitive functions and selective attention were associated with lower scores on the risk of falls measured by the total score of the Tinetti Scale (R² = 0.102).

Table 3.

Multivariate linear regression analyses for factors associated with gait parameters, functional mobility, and dynamic balance

Variable		B	β	t	95% CI		p-Value
Grip strength (dynamometry)	Cognitive impairment (MMSE)	0.618	0.208	2.395	0.107	1.129	0.018
R2 of the model							0.043
Physical Performance (TUG Test)	Attention and speed (TMT-A)	0.634	1.819	2.451	0.122	1.147	0.016
	Executive functions (TMT-B)	−0.134	−2.635	−2.686	−0.233	−0.035	0.008
R2 of the model							0.078
Gait speed (TUG test score formula)	Cognitive function (MMSE)	0.054	0.680	2.023	0.001	0.106	0.045
	Attention and speed (TMT-A)	−0.017	−1.601	−2.356	−0.32	−0.003	0.020
	Executive functions (TMT-B)	0.004	2.679	3.307	0.001	0.007	0.003
	Concentration (d2)	−0.017	−0.895	−2.115	−0.034	−0.001	0.037
R2 of the model							0.256
Balance confidence (ABC-16)	Executive functions (TMT-B)	0.029	0.174	1.989	0.000	0.057	0.049
R2 of the model							0.079
Fear of falling (FES-I)	Cognitive impairment (MoCA)	7.946	1.334	2.504	1.664	14.228	0.014
	Concentration (d2)	1.728	0.842	2.008	0.025	3.431	0.047
	Age	−0.671	−0.257	−2.998	−1.114	−0.228	0.003
R2 of the model							0.144
Balance (Balance _ Tinetti)	Concentration (d2)	0.089	0.245	2.846	0.027	0.151	0.005
R2 of the model							0.074
Gait (Gait _ Tinetti)	Cognitive function (MMSE)	0.237	0.176	2.012	0.004	0.471	0.046
	Verbal fluency (Issac Test)	0.201	0.197	2.269	0.026	0.376	0.025
	Selective attention (d2)	0.082	0.199	2.288	0.011	0.152	0.024
R2 of the model							0.198
Risk of falls (Total Score Tinetti)	Cognitive function (MMSE)	−1.579	−0.776	−2.047	−3.106	−0.051	0.043
	Verbal fluency (Issac Test)	0.927	0.170	1.974	−0.003	1.858	0.049
R2 of the model							0.102

MMSE the mini-mental state examination, MoCA the montreal cognitive assessment, TMT trail making test, DSST the digit symbol substitution test, TUG the timed up-and-go test, ABC-16 the activities-specific balance confidence, FES-I the falls efficacy scale-international

Discussion

This study analyzed the relationship between cognitive function and various physical and psychological factors associated with risk of falls in older adults with MCI. The risk of falls was assessed using three validated tools: the Tinetti Test, which evaluates gait and balance; the ABC-16 scale, which measures balance confidence; and the FES-I, which evaluates fear of falling during daily activities. Although significant, most associations observed between cognitive variables and physical or psychological fall-related factors were of small magnitude. This suggests that, while cognitive function plays a role, it likely explains only a limited portion of the variance in physical performance outcomes. These results highlight the multifactorial nature of risk of falls in older adults and support the need for multidimensional assessments.

Muscle strength is a fundamental component for mobility and postural stability in older adults, and its decline, known as sarcopenia, is identified as a risk factor for functional and cognitive decline [56]. In this study, a significant correlation was observed between muscle grip strength and various aspects of cognitive function, suggesting that decreased grip strength may be associated with cognitive decline in older adults with MCI. Specifically, grip strength showed positive correlations with performance on tests of cognitive function, cognitive impairment, attention and speed, executive functions, selective attention, and processing speed. These results indicate that greater muscle grip strength is associated with better performance in these key cognitive areas. This finding reinforces the idea that muscle strength may be an early indicator of cognitive decline. Although previous literature has documented the relationship between physical activity and brain plasticity, this study expands our understanding by suggesting that grip strength is not only important for mobility but also for cognitive efficiency in older adults with MCI. These findings are consistent with previous studies suggesting that muscle grip strength may help preserve higher cognitive skills such as planning, decision-making, and problem-solving [57]. Furthermore, the study shows that grip strength is also correlated with cognitive decline, suggesting that its loss could reflect general alterations in physical health, indirectly affecting the brain through mechanisms such as reduced neuroplasticity or metabolic dysfunction [58]. This study found no significant correlations between grip strength and variables such as age, sex, BMI, falls, or tobacco use, suggesting that the observed effects between muscle strength and cognitive function are independent of these common factors in the older adult population. This finding highlights the importance of grip strength as a potential predictor of cognitive function, underscoring its relevance not only in terms of mobility but also in terms of cognitive health.

Physical function not only affects older adults' ability to perform daily activities but also appears to have a significant impact on the preservation of cognitive function [59]. In this study, physical function was found to be positively correlated with several areas of cognitive function, suggesting that greater physical capacity could be linked to better cognitive performance in older adults with MCI. A notable finding was the significant correlation between physical function and cognitive impairment, suggesting that greater physical capacity could be associated with a lower risk of cognitive decline. This finding is consistent with previous research indicating that physically fit individuals are less likely to develop cognitive deficits, in part due to the positive effects of physical activity on neuroplasticity and brain health [60, 61].

This study expands the literature by suggesting that better physical performance could act as a protective factor against cognitive decline, helping to maintain healthy brain function during aging [62]. A particularly interesting finding was the significant correlation between physical function and verbal fluency, which is an important measure of cognitive function associated with the ability to generate words quickly and is closely linked to executive function. This suggests that older adults with better physical ability may also be more cognitively agile, as verbal fluency depends on the efficiency of memory access and the rapid activation of brain networks [63].

Similarly, a significant correlation was observed between physical function and selective attention and processing speed. Selective attention, which allows people to filter out irrelevant stimuli and focus on specific tasks [64], and processing speed, which measures how quickly a person processes information, are fundamental skills for performing everyday tasks [65]. These results support the hypothesis that physical function could be associated with greater cognitive efficiency in tasks requiring speed and concentration, as its performance has been associated with improvements in cerebral circulation and the release of neurotrophic factors, which could facilitate these cognitive processes [66, 67]. Similarly, physical function showed a significant correlation with concentration, underlining the impact of physical activity on preserving concentration-related cognitive function in the long term [68].

Gait speed is a key indicator of functional mobility in older adults and has been recognized as a potential marker of overall health status and brain aging [69]. In this study, a slower gait speed was found to be associated with poorer cognitive performance, including global cognitive function and cognitive performance. This finding is consistent with previous studies that have identified gait speed as a predictor of cognitive decline and dementia [70, 71]. This could reflect dysfunction in the neural networks involved in motor planning and executive control, especially in the prefrontal cortex and basal ganglia, areas also implicated in cognitive processes [72]. A particularly relevant finding was the negative correlation between gait speed and verbal fluency, suggesting that participants with slower gait speed performed worse on tasks requiring rapid memory access and executive control of language. This could be due to the involvement of the frontal lobe in both processes: movement regulation and language function [73]. Likewise, significant correlations were found between gait speed and specific cognitive functions such as attention and speed, executive function, selective attention, and processing speed, suggesting that all of these processes depend on the efficiency of neuronal transmission and could be affected by brain aging and early neurodegeneration [74]. Finally, one of the most significant results regarding gait speed is its relationship with concentration, indicating that participants with a slower gait have a lower ability to maintain sustained attention, reinforcing the idea that motor control and attention regulation share common neurobiological mechanisms, especially in the connections between the prefrontal cortex and subcortical circuits [75]. Fear of falling is a psychological factor that affects both mobility and quality of life in older adults, influencing physical activity and balance confidence [76]. In this study, a significant correlation was found between fear of falling and several cognitive functions, suggesting that this fear not only affects mobility but also cognitive performance. This finding is consistent with previous studies, such as that of Delbaere et al. [77], which showed that fear of falling can limit mobility, reduce cognitive stimulation, and increase the risk of neurodegenerative decline. Likewise, a significant correlation was found with verbal fluency, indicating that participants with a greater fear of falling may have difficulties with rapid language access and cognitive flexibility. Verbal fluency is closely linked to executive function and attentional control, which could explain this relationship [78]. Furthermore, significant associations were observed with concentration, selective attention, and processing speed, suggesting that fear of falling may affect older adults' ability to maintain attention on specific tasks and process information efficiently. This could be related to increased cognitive load due to constant concerns about stability and the risk of falls [79]. Finally, a positive correlation was found between fear of falling and BMI, suggesting that heavier individuals may experience greater perceptions of instability, which in turn increases their fear of falling. Previous studies have indicated that overweight and obesity can affect postural control and gait biomechanics, which could explain this relationship [80, 81].

Balance, gait, and fall risk are closely interrelated factors in the mobility of older adults and were assessed in this study using the Tinetti Scale. Our results indicate that these components are significantly associated with cognitive performance, supporting previous evidence of a close relationship between motor and brain function. Specifically, positive correlations were observed between postural balance scores and several cognitive domains, including verbal fluency, concentration, and selective attention, indicating that better balance performance (higher Tinetti scores) is associated with better cognitive functioning [73]. Regarding gait, significant associations were found between gait performance and most of the cognitive functions assessed, particularly concentration, attention and processing speed, and verbal fluency. These findings suggest that a more stable and efficient gait pattern is linked to greater cognitive efficiency, which is consistent with previous research identifying reduced gait performance as an early marker of cognitive decline [82, 83]. Importantly, lower total Tinetti scores, reflecting poorer balance and gait performance and, therefore, a higher risk of falls, showed the strongest associations with cognitive impairment. Specifically, greater fall risk (lower Tinetti scores) was associated with worse performance in verbal fluency, concentration, attention, and processing speed. These results reinforce the notion that individuals at higher risk of falls may exhibit more pronounced cognitive decline, potentially due to impairments in motor planning and postural control, functions that largely depend on prefrontal cortical and subcortical networks [84].

This study has several relevant clinical implications for the care of older adults with MCI. Although most of the observed associations were of small magnitude, their overall pattern highlights the importance of adopting a comprehensive and multidimensional approach to risk of falls assessment that integrates both cognitive and physical variables. In particular, cognitive domains related to executive function, attention, concentration, and processing speed showed the most consistent associations with fall-related outcomes such as gait performance, balance, balance confidence, and fear of falling. From a practical clinical perspective, these findings suggest that cognitive processes involved in attentional control, executive functioning, and information processing speed may be especially relevant when identifying older adults with MCI who are at increased risk of functional decline and falls. While global cognitive screening remains important, incorporating brief assessments of these specific cognitive domains alongside physical performance measures may help clinicians better characterize individual risk profiles. Nevertheless, given the modest effect sizes observed, these cognitive domains should not be considered isolated predictors but rather components of a broader, multidimensional assessment framework. Finally, the results support the potential value of personalized preventive strategies that integrate physical training and cognitive stimulation, as well as the need for periodic assessments and follow-up to detect changes in functional and cognitive status over time, enabling timely and targeted interventions. In line with the multidimensional nature of fall risk, combined intervention approaches targeting both cognitive and physical domains may be particularly promising for older adults with mild cognitive impairment. Previous research suggests that multicomponent physical exercise programs, including balance training, strength exercises, and gait training, can improve mobility and reduce fall risk, especially when tasks involve postural control and dual-task demands. At the cognitive level, interventions focusing on executive function, attention, and processing speed, such as cognitive training or task-oriented cognitive stimulation, may be especially relevant given their role in motor planning and safe mobility. Importantly, emerging evidence supports the potential benefits of integrated interventions that combine physical exercise with cognitive stimulation, for example through dual-task training, exergames, or structured programs that simultaneously challenge motor and cognitive processes. Such approaches may enhance motor, cognitive integration and functional adaptability, which are critical for preventing falls in real-world environments. Although the present study cannot determine the effectiveness of specific interventions, these findings support the rationale for future longitudinal and interventional studies to evaluate combined cognitive, physical programs as a strategy to reduce fall risk in older adults with mild cognitive impairment.

Furthermore, this study has some limitations that should be considered. Due to its cross-sectional design, it is not possible to establish cause-and-effect relationships between cognitive impairment and physical or functional outcomes. Therefore, the directionality of the observed associations cannot be determined. While poorer cognitive performance may contribute to declines in physical function and increased fall risk, the opposite direction cannot be ruled out. For example, reduced mobility, balance impairment, or fear of falling may lead to decreased physical activity, social participation, and environmental interaction, which in turn could negatively affect cognitive functioning over time. Additionally, because the study was conducted in men and women over 60 years of age with mild cognitive impairment from a specific geographic region, the findings may not be generalizable to populations with different demographic or clinical characteristics. Although several associations reached statistical significance, the effect sizes were generally small, which may limit the clinical relevance of individual predictors. Future studies using longitudinal designs and larger sample sizes are warranted to clarify the temporal sequence of these relationships and to explore potential mediators or moderators. Another limitation relates to the assessment of falls based on self-report over a one-month recall period. Although this approach is pragmatic and commonly used in clinical and epidemiological research, it may be subject to recall bias, particularly in older adults with mild cognitive impairment. Short recall periods may reduce memory-related errors but could underestimate fall occurrence, whereas longer recall periods might increase reporting inaccuracies. In this context, the choice of recall period may influence the strength of the observed associations. Future studies could benefit from incorporating prospective methods, such as fall diaries or continuous monitoring, to provide more accurate and temporally precise information on fall events. Another limitation is that gait speed was not assessed using a direct linear walking test but was instead estimated through a correction formula applied to the Timed Up-and-Go test. Although this approach has been validated and used in previous studies, it provides a derived rather than a direct measure of gait speed, which may affect measurement precision. Finally, unmeasured factors such as nutritional status, psychological stress, or undiagnosed medical conditions may have influenced the observed associations between cognitive and physical outcomes. Future research should consider these additional variables when examining fall risk and mobility in older adults with mild cognitive impairment.

Conclusions

The findings of this study suggest an association between motor function and cognitive performance in older adults with mild cognitive impairment. Variables such as grip strength, gait speed, balance, balance confidence, and fear of falling were related to different cognitive domains, including verbal fluency, attention, processing speed, and executive function. In particular, slower gait speed and poorer balance and gait performance, reflecting a higher risk of falls, were associated with worse cognitive performance. These results are consistent with the notion that motor and cognitive functions may be interrelated and could share common underlying mechanisms. From a clinical perspective, these findings support the relevance of considering physical and functional variables when assessing older adults with mild cognitive impairment. Although causal inferences cannot be drawn due to the cross-sectional design, identifying deficits in gait, balance, and balance confidence may help to better characterize risk profiles in this population. Future longitudinal studies are needed to determine whether combined physical and cognitive assessments can contribute to early identification of individuals at greater risk of functional decline and to inform the development of targeted preventive strategies.

Author contributions

Conceptualization, M.D.M.C.-F. and A.A.-A.; methodology, F.H.-C. and M.D.C.C.-F.; formal analysis, A.A.-A. and F.H.-C.; writing-original draft preparation, M.D.M.C.-F. and Y.C.-C.; writing—review and editing, A.A.-A. and Y.C.-C; supervision, F.H.-C. and M.D.C.C.-F. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data availability

The data presented in this study are available upon request from the corresponding author. The data are not publicly available due to the sensitive nature of the questions asked in the study. All participants were assured that the data collected would remain confidential and would not be shared.

Declarations

Institutional review board

The research was approved by the Ethics Committee of the University of the Middle Atlantic (CEI05-012, approval date: 3 December 2024) and was conducted in accordance with the principles of the Declaration of Helsinki, good clinical practice standards, and current legislation.

Informed consent

Informed consent was obtained from all subjects involved in the study.

Conflicts of interest

The authors declare no conflicts of interest.