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. 2026 Aug 27;12:30495334261478667. doi: 10.1177/30495334261478667

Older People at High Risk of Falls With and Without Cognitive Impairment and Factors Related to Falls: A Cross-Sectional Study

Daiene de Morais 1,✉, Madson Alan Maximiano-Barreto 2, Luana Flávia Talmelli-Ruy 1, Ana Laura Baldi 1, Caroline Barbalho Lamas 1, Ana Laura Oliveira Dias 1, Karina Gramani-Say 1, Juliana Hotta Ansai 1
PMCID: PMC13522682  PMID: 42666693

Abstract

Cognitive impairment in older adults is associated with reduced functionality, impaired mobility, and increased fall risk, although the mechanisms linking these factors remain unclear. This study compared clinical and sociodemographic characteristics of community-dwelling older adults at high risk of falls with and without cognitive impairment and identified factors associated with the number of falls in each group. In this cross-sectional study, 118 older adults were assessed and classified as having cognitive impairment using the Mini-Mental State Examination and/or ACE-R. Sociodemographic, clinical, cognitive, psychological, functional, mobility (single- and dual-task Timed Up and Go), and fall history data were collected. Older adults with cognitive impairment (n=63) showed worse cognitive, functional, and mobility performance than those without impairment (n=55), although no difference was observed in the number of falls (p>0.05). Simple linear regression revealed that self-rated health was the only predictor of falls among individuals with cognitive impairment, whereas the number of medications predicted falls in those without impairment. Cognitive impairment is associated with greater multidimensional vulnerability, and fall-related factors differ between groups, indicating distinct risk mechanisms.

Keywords: falls, cognition, aging, functional assessment

1. Introduction

Population aging is a global phenomenon with significant implications for health systems, especially given the increased prevalence of chronic conditions, functional decline, and greater vulnerability to adverse events, such as falls (Beard et al., 2016; Hajizadeh et al., 2025). Approximately 28% to 35% of people aged 65 or older experience at least one fall per year, and this proportion can exceed 40% among individuals over 80 years of age (World Health Organization [WHO], 2021). In Brazil, falls represent one of the main causes of morbidity and mortality in older people and are associated with hospitalizations, loss of functional independence, and reduced quality of life (Pereira da Costa et al., 2020).

Several factors contribute to the risk of falls among older adults, including physiological changes associated with aging, the use of multiple medications, the presence of chronic conditions, balance deficits, and unfavorable environmental conditions (e.g., inadequate lighting, uneven floors, use of inappropriate footwear, among others) (Carvalho et al., 2021). Among these factors, cognitive impairment has been identified as an important predictor of falls, as it influences attention, postural control, and the ability to judge the environment (Alkhamis et al., 2025). In addition, individuals with cognitive impairment tend to exhibit subtle changes in balance and gait, even before manifesting symptoms of dementia, which reinforces the need for early monitoring (ANSAI et al., 2017).

Older adults with cognitive impairment are up to twice as likely to fall compared to those who are cognitively preserved (Montero-Odasso et al., 2020). In a longitudinal study, a strong association was observed between reduced cognitive performance and postural instability (Ansai et al., 2020), suggesting that cognitive interventions may play a protective role against falls. In the Brazilian context, investigations carried out in community-dwelling older adults have shown that variables such as frailty, polypharmacy, and poor performance on mobility tests are significant predictors of recurrent falls (Borges et al., 2023; Fallaci et al., 2022).

Although considered an important risk factor for falls, differences in assessment methods and in how falls are measured make comparisons among studies on this topic challenging (Leroy et al., 2023). Since the accumulation of risk factors increases the likelihood of falls (Taylor et al., 2014), early identification that takes into account these risk factors is a necessary strategy in health services. Furthermore, understanding the coexisting variables in older adults at high risk of falls with and without cognitive impairment can help monitor both variables and, consequently, reduce the likelihood of new fall events.

The comparative analysis between older adults with and without cognitive impairment allows the identification of specific factors that increase the risk of falls in these subgroups, providing support for personalized prevention and rehabilitation strategies. This approach is especially relevant in the context of Primary Health Care, where the early detection of cognitive and mobility deficits can reduce complications and promote safer and more autonomous aging (Borges et al., 2024 ). Thus, the present study aims to verify the clinical and sociodemographic differences between older adults at high risk of falls, considering the presence or absence of cognitive impairment, in addition to identifying possible risk factors for falls in both groups.

2. Method

2.1. Study Design and Setting

This is a cross-sectional study linked to initial data from two versions of the Multidimensional and Care-Based Management Program for Older Adults (Morais et al., 2025; Sossai et al., 2024). The MAGIC Program is a randomized, single-blind, parallel-group controlled clinical trial. Between 2021 and 2025, non-institutionalized older adults in Brazil with a history of at least one fall in the last year were recruited.

2.2. Procedures

Participants were recruited by telephone during business hours. Contacts were obtained from health units at the primary, secondary, and tertiary levels, as well as social and educational services of different levels of complexity. Information from the MAGIC program database (Morais et al., 2025; Sossai et al., 2024) and some community groups were also used. Dissemination was carried out through pamphlets, posters, radio, television, and social networks.

The study was approved by the Research Ethics Committee of the Federal University of São Carlos (CAAE: 34350620.7.0000.5504 and CAAE: 86710225.5.0000.5504) and registered in the Brazilian Registry of Clinical Trials (REBEC: U1111-1314-4783). All volunteers signed the informed consent form. The study followed the recommendations of STROBE (VON ELM et al., 2008).

2.3. Participants

The final sample consisted of 118 older individuals, 63 with cognitive impairment (CIG) and 55 without cognitive impairment (wCIG). Flowchart 1 describes the included participants (Figure 1).

Figure 1.

Figure 1.

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Flow diagram of the study participants

For inclusion criteria, the following were considered: level of consciousness that allows the volunteer to be an active subject in the intervention; high risk of falls according to the World Recommendation on Fall Prevention and Management in Older Persons (Montero-Odasso et al., 2022), that is, older adults with a fall and one or more of the following characteristics: (i) accompanying injury, (ii) multiple falls (≥2 falls) in the previous 12 months, (iii) known frailty, (iv) inability to get up after the fall without help for at least an hour and (v) accompanied by (suspected) transient loss of consciousness; ability to walk independently, with or without walking aids, and willingness and availability to take part in the proposed assessments.

The exclusion criteria were: severe uncorrected hearing and/or visual deficits; Presenting neurological diseases that rapidly modify the risk of falls, including Multiple Sclerosis, diagnosis of moderate to advanced dementia, epilepsy, and traumatic brain injury, or taking associated medications; and presenting any contraindication to performing the procedures (e.g., acute infectious disease, dissecting aortic aneurysm, severe aortic stenosis, congestive heart failure/unstable angina, acute myocardial infarction, acute myocarditis, acute pulmonary or systemic embolism, thrombophlebitis, ventricular tachycardia, and other dangerous arrhythmias).

The sample was divided into older people with cognitive impairment (CIG) and without cognitive impairment (wCIG). People with cognitive impairment were considered to be those who scored below the cutoff score for their educational level on the Mini-Mental State Examination (MMSE) (BRUCKI et al., 2003) and/or scored below 78 points on the Addenbrooke’s Cognitive Examination–Revised (ACE-R) (CARVALHO, 2009).

The sample size calculation was performed using the G*Power 3.1 statistical program, considering (1) the objective of identifying differences between groups regarding fall risk factors; (2) the test used (independent t-test); (3) the main measure (fall risk factors); (4) a type I error of 5%; (5) a statistical power of 80%; and (6) an effect size of 0.6. Thus, considering a 20% chance of sample loss, a minimum of 108 participants was determined to ensure a final sample of 90 individuals.

2.4. Outcomes

Initially, an anamnesis was conducted, containing clinical and sociodemographic data, with information on age, sex, race, marital status, years of schooling, whether they live alone, income, number of medications in use, presence of morbidities, self-assessment of health, and history of falls (number of falls in the last year, location, and consequence). The Self-Assessment of Health was performed using a single question that asks the older person about their perception of their overall health status and has five response options (very good/good/fair/poor/inferior) that can vary, with scores ranging from five (very good) to one point (very poor) (Mantovani et al., 2015). A fall was defined as “an event, which results in a person coming to rest inadvertently on the ground or other level. It can be a consequence of the following: Sustaining a violent blow, loss of consciousness, sudden onset of paralysis, as in a stroke, an epileptic seizure” (Kellogg International Work Group on the Prevention of Falls by the Elderly, 1987).

A multidimensional assessment of fall risks was conducted, including cognition, depression, anxiety, concern about falling, and functional measures (Montero-Odasso et al., 2022). For cognitive assessment, the Addenbrooke’s Cognitive Examination - Revised (ACE-R) and the Digit Span tools were used. The ACE-R consists of a brief cognitive assessment battery, ranging from 0 to 100 points. The scoring is distributed across cognitive domains: orientation and attention (18 points), memory (26 points), verbal fluency (14 points), language (26 points), and visuospatial skills (CARVALHO, CARAMELLI, 2007). In addition, this tool also includes the Mini-Mental State Examination (MMSE) and the Clock Drawing Test (CDT). In the CDT, the volunteer is asked to draw a clock with hands, showing two hours and 45 minutes. The test is scored considering the outline of the clock and the placement of all the numbers and hands (Sunderland et al., 1989). The score ranges from one to 10 points, with a score of six or less indicating very poor performance and between nine and 10 points indicating good performance. The Digit Span consists of seven pairs of numerical sequences with different numbers of digits; this test is applied in direct and reverse order. In the direct application, the sequences have from 3 to 9 numbers and, in the inverse, they have from 2 to 8 numbers (Strauss et al., 2006). The test ends after an error of two consecutive sequences. The maximum number of repeated numbers without an error is recorded for each version.

To assess depression, anxiety, and concern about falling, the Geriatric Depression Scale (GDS), the Geriatric Anxiety Inventory (GAI), and the Falls Efficacy Scale-International (FES-I) were used. In its reduced version with 15 items, the GDS has a range from zero (absence of depressive symptoms) to fifteen points (maximum score of depressive symptoms) (Yesavage et al., 1982). The GAI consists of a brief tool for assessing anxiety in older people (Martiny et al., 2011). The FES-I has 16 items ranging from 1 (not at all concerned) to 4 (very concerned). It is suggested that a score greater than or equal to 23 is associated with sporadic falls, while a score greater than 31 points is associated with recurrent falls (CAMARGOS et al., 2010).

To assess the level of independence in instrumental activities of daily living, the Lawton & Brody Scale was used. The tool assesses the older person’s ability to perform essential daily tasks, including feeding, bathing, dressing, sphincter control, transferring, and moving independently. The total score ranges from 0 to 21 points, with higher scores indicating greater functional independence (Santos; Virtuoso Junior, 2008). Furthermore, to assess functional mobility, the simple Timed Up and Go (TUG) test was used at the volunteer’s usual speed (ALEXANDRE et al., 2012) and associated with a cognitive task. Thus, while the volunteer completed the course, the individual was instructed to name animals without stopping the test.

2.5. Data Analysis

Descriptive analyses of the data and point and interval estimations of the parameters of interest were performed. A significance level of p=0.05 was considered, and the SPSS software (30.0) was used to perform the statistical tests. The Kolmogorov-Smirnov normality test was applied to all continuous variables to verify the data distribution. As most of the data did not present a normal distribution, to compare the groups regarding clinical and sociodemographic characteristics, including fall risk factors, the Chi-square test of association was used for categorical variables and the Mann-Whitney test for continuous variables with non-normal distribution.

To verify the fall risk factors and the number of falls for each group (CIG and wCIG), simple linear regression was first used (F probability for removal ≥ 0.100). Since no more than one significant association was found in the simple regression analysis (p<0.05), backward stepwise multiple linear regression was not used.

3. Results

The clinical and sociodemographic characteristics of the total sample and of each group are presented in detail in Table 1. The total sample consisted of 84.7% female participants, with a mean age of 72.32 years (±7.96) and 9.14 years of schooling (±5.54). Statistically significant differences between the CIG and wGIC groups were observed for the variables age, education, and polypharmacy (p=<0.01), in which the CIG group had the highest mean age and the highest frequency of polypharmacy, while the wCIG group had the highest level of education.

Table 1.

Clinical and Sociodemographic Characteristics of Older People With and Without Cognitive Impairment (n=118)

Variables n (%)/M±SD Total (n=118) Groups p value
CIG (n=63) wCIG (n=55)
Women 100 (84.7) 54 (85.7) 46 (83.6) 0.754
Age (years) 72.32±7.96 74.50±8.20 69.70±7.00 <0.010*
Years of schooling 9.14±5.54 6.10±4.80 12.60±4.90 <0.010*
Race
White 89 (75.4) 44 (69.8) 45 (81.8) 0.213
Brown 21 (17.8) 15 (23.8) 6 (10.9)
Black 7 (5.9) 3 (4.8) 4 (7.3)
Yellow 1 (0.8) 1 (1.6) 0 (0)
Marital status
Married 48 (40.7) 25 (39.7) 23 (41.8) 0.345
Single 9 (7.6) 3 (4.8) 6 (10.9)
Widow 42 (35.6) 27 (42.9) 15 (27.3)
Separated/Divorced 14 (11.9) 6 (9.5) 8 (14.5)
Other 5 (4.2) 2 (3.2) 3 (5.5)
Living alone 31 (26.3) 13 (20.6) 18 (32.7) 0.137
Family income #
Up to 1 minimum wage 14 (12.7) 6 (10.3) 8 (15.4) 0.647
1 to 3 minimum wages 48 (43.6) 26 (44.8) 22 (42.3)
4 to 6 minimum wages 35 (31.8) 21 (36.2) 14 (26.9)
7 or more 13 (11.8) 5 (8.6) 8 (15.4)
Self-assessment of health
Very good 12 (10.2) 7 (11.1) 5 (9.1) 0.120
Good 52 (44.1) 24 (38.1) 28 (50.9)
Average 47 (39.8) 25 (39.7) 22 (40.0)
Poor 6 (5.1) 6 (9.5) 0 (0)
Very poor 1 (0.8) 1 (1.6) 0 (0)
Polypharmacy 40 (33.9) 28 (44.4) 12 (21.8) <0.010*
Number of diseases 2.26±1.26 2.26±1.26 2.2±1.3 0.683
Systemic Arterial Hypertension 55 (46.6) 33 (52.4) 22 (40.0) 0.179
Diabetes Mellitus 41 (34.7) 22 (34.9) 19 (34.5) 0.966
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#Minimum wage of R$ 1518.00.

*p<0.05.

n (%): number of individuals (percentage); M±SD: mean±standard deviation; CIG: Cognitive Impairment Group; wCIG: without Cognitive Impairment Group; X2: Chi-square test; U: Mann-Whitney U test.

Regarding the history and characteristics of falls in the last year, no statistically significant differences were found between the groups, as shown in Table 2. Most falls occurred while in bed, and the most serious consequence was an injury requiring stitches.

Table 2.

Number of Falls and Their Characteristics (n=118)

Variables, n (%)/M±SD Total (n=118) Groups P value
CIG (n=63) wCIG (n=55)
Number of falls in the last year 2.85±2.26 2.98±2.77 2.71±1.52 0.960
Location of the last fall
Bed 24 (20.3) 14 (22.2) 10 (18.2) 0.187
Chair or armchair 12 (10.2) 5 (7.9) 7 (12.7)
Shower chair and/or toilet 2 (1.7) 1 (1.6) 1 (1.8)
Own height 14 (11.9) 10 (15.9) 4 (7.3)
Tree 10 (8.5) 7 (11.1) 3 (5.5)
Stairs 7 (5.9) 4 (6.3) 3 (5.5)
Roof 17 (14.4) 9 (14.3) 8 (14.5)
Other 18 (15.3) 8 (12.7) 10 (18.2)
Most serious consequence of the last fall
None 21 (18.4) 12 (20.0) 9 (16.7) 0.951
Abrasions 18 (15.8) 10 (16.7) 8 (14.8)
Stitched wound 41 (36.0) 18 (30.0) 23 (42.6)
Closed fracture 4 (3.5) 1 (1.7) 3 (5.6)
Open fracture 15 (13.2) 11 (18.3) 4 (7.4)
Sprain and dislocation 14 (12.3) 8 (13.3) 6 (11.1)
Neurological injury 1 (0.9) 0 1 (1.9)
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n (%): number of individuals (percentage); M±SD: mean±standard deviation; CIG: Cognitive Impairment Group; wCIG: without Cognitive Impairment Group; X2: Chi-square test; U: Mann-Whitney U test.

Table 3 presents the cognitive, psychological, and functional characteristics of older adults with and without cognitive impairment. Regarding cognition, there were statistically significant differences between the groups, with consistently lower averages for the group with impairment, as well as in the assessment of instrumental capacity for daily living. As for psychological characteristics, no differences were found between the groups regarding depressive symptoms (p=0.342) and anxiety (p=0.134). The same pattern was observed for concern about falling (FES-I), with no significant difference between the groups (p=0.152). In the domain of functional mobility, the CIG performance was inferior in both the single TUG and the dual-task TUG, although only the dual-task test reached statistical significance. Regarding fall risk factors (number of falls) for the CIG, the only significant influence was on self-rated health (B (95% confidence interval) = -0.877 (-1.675 - -0.080); R2 = 0.077, p-value = 0.032). For the wCIG, there was a significant influence of the number of medications (B (95% confidence interval) = 0.169 (0.028 - 0.310); R2 = 0.098, p-value = 0.020) on the number of falls. These results suggest that the determinants of the number of falls differ according to the cognitive profile.

Table 3.

Cognitive, Psychological, and Functional Characteristics of Older Adults With and Without Cognitive Impairment (n=118)

Variables, M±SD Total (n=118) Groups p value
CIG (n=63) wCIG (n=55)
Cognition
ACE-R 72.68 (16.70) 63.02 (14.90) 83.41 (12.40) <0.010*
MMSE 26.06 (3.83) 24.21 (3.80) 28.24 (2.40) <0.010*
CDT 28.35 (8.60) 25.45 (8.02) 32.09 (8.12) <0.010*
Digit Span (direct) 5,21 (1.20) 4.79 (1.02) 5.69 (1.19) <0.010*
Digit span (inverse) 3,73 (0.98) 3.33 (0.90) 4.19 (0.87) <0.010*
Depression and anxiety
GDS 4,02 (2.87) 4.29 (2.83) 3.71 (2.93) 0.342
GAI 7,14 (5.23) 7.92 (5.33) 6.26 (5.03) 0.134
Concern about falling: FES-I 29,52 (8.80) 30.74 (9.08) 28.08 (8.30) 0.152
Functional capacity: Lawton & Brody scale 20,34 (3.36) 18.62 (3.61) 22.38 (2.08) <0.01*
Functional mobility
TUG 16,81 (8.23) 18.85 (8.40) 14.08 (7.60) 0.239
TUG with dual task 21,95 (11.83) 26.59 (12.43) 15.77 (7.89) <0.01*
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M±SD: mean±standard deviation; CIG: Cognitive Impairment Group; wCIG: without Cognitive Impairment Group; U: Mann-Whitney U test; ACE-R: Addenbrooke’s Cognitive Examination - Revised; MMSE: Mini-mental State Examination; CDT: Clock Drawing test; GDS: Geriatric Depression Scale; GAI: Geriatric Anxiety Scale; FES-I: Falls Efficacy Scale-International; TUG: Timed Up and Go test.

*p<0.05.

4. Discussion

This study aimed to compare clinical, cognitive, psychological, and functional characteristics among older individuals at high risk of falls, divided into two groups (with and without cognitive impairment), and to investigate factors associated with the number of falls in each group. The results showed that, although both groups presented a high risk, cognitive impairment was associated with older age, lower education level, higher prevalence of polypharmacy, worse overall cognitive performance, greater functional dependence, and worse performance on dual-task mobility tasks. However, interestingly, the number of falls did not differ between the groups, suggesting distinct risk mechanisms, which reinforces the need for personalized approaches.

The differences observed in cognitive and functional performance between the groups corroborate evidence that mild or early cognitive impairment is associated with poorer performance on tasks dependent on attention, working memory, and executive control. Recent studies reinforce that deficits in these domains directly affect gait and balance, especially under dual-task conditions, increasing postural instability and the risk of falls (Montero-Odasso et al., 2020; ANSAI et al., 2021a; b). Thus, the poorer performance of the CIG in the ACE-R, MMSE, CDT, Digit Span, and dual-task TUG points to a profile of greater neurofunctional vulnerability. Despite these differences, the number of falls did not vary significantly between the groups. This finding may reflect a “risk ceiling” effect, as the entire sample consisted of older adults classified as high risk of falls. Longitudinal studies in similar populations show that when a history of falls is already an inclusion criterion, intergroup differences tend to manifest more strongly only at follow-up, and not at baseline (Beck Jepsen et al., 2022; Taylor et al., 2021).

Corroborating our findings, European and Canadian studies report lower functional performance patterns in older adults with cognitive impairment, even in the absence of dementia (Schmidt et al., 2024). However, these differ from Asian research, which found significant differences in the number of falls, possibly due to distinct environmental and cultural contexts (Li et al., 2024). The meta-analysis by (Pereira da Costa et al., 2020) highlighted that a longer time on the Timed Up and Go (>12 seconds) predicts recurrent falls, regardless of muscle strength, suggesting that functional performance reflects the integration between postural control, cognitive processing, and motor confidence. From a neurophysiological point of view, it is plausible to assume that alterations in brain regions involved in motor control and attention, such as the prefrontal cortex and basal ganglia, contribute to the relationship between cognitive decline and falls (Mirelman et al., 2022). Furthermore, executive and processing speed deficits can compromise an older person’s ability to respond quickly to balance disturbances or unpredictable environmental situations (AMBONI; BARONE; HAUSDORFF, 2013).

One possible hypothesis for these findings is that the interaction between cognition and mobility can be explained by the functional cognitive reserve model, according to which reduced executive capacity impairs the allocation of attentional resources during postural control. This hypothesis has been supported by neuroimaging studies showing lower prefrontal activation in older people with mild cognitive impairment during dual-task gait tasks (Montero-Odasso et al., 2018). On the other hand, it is important to consider that the association between cognition and falls may be partially mediated by gait alterations and concern about falling (ANSAI et al., 2021a; b; Ellmers, Delbaere & Kal, 2023). Thus, the impact of cognitive impairment on falls should be understood within a broader biopsychosocial model that includes motor, psychological, and clinical factors.

In the regression analysis, self-rated health was the only significant predictor of the number of falls among older adults with cognitive impairment. The literature suggests that self-rated health integrates multiple subjective perceptions related to physical and psychological well-being and is a robust marker of disability, functional decline, and mortality (St John et al., 2023). Thus, in individuals with cognitive impairment, a worse perception of health may reflect the accumulated presence of physical frailty, cognitive decline, and chronic conditions, influencing the occurrence of falls. For the group without cognitive impairment, the number of medications was the only significant predictor of falls. Recent systematic reviews point to polypharmacy and the use of potentially inappropriate medications as central factors for adverse outcomes in older adults, including falls (Seppala et al., 2022). The findings of the present study reinforce the importance of the review.

5. Conclusion

Although individuals with cognitive impairment perform worse across multiple dimensions, the factors associated with the number of falls differ between groups, suggesting distinct risk mechanisms. Among those with cognitive impairment, subjective factors related to health perception appear more relevant, while in cognitively preserved older adults, pharmacological aspects play a central role. These findings reinforce the need for personalized fall prevention strategies, with interventions that consider the clinical and cognitive heterogeneity of older populations. Multicomponent interventions that combine cognitive training, balance training in the context of cognitive overload, and multidimensional assessment may be more effective.

Author Note: This study was conducted by Federal University of São Carlos, Department of Gerontology, São Carlos – SP, Brasil.

Funding: The work was supported by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, (001), Fundação de Amparo à Pesquisa do Estado de São Paulo, (2024/10699-6, 2024/23097-4).

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

ORCID iDs

Daiene de Morais https://orcid.org/0000-0001-8609-1190

Madson Alan Maximiano-Barreto https://orcid.org/0000-0002-1314-9846

Juliana Hotta Ansai https://orcid.org/0000-0001-9873-3509

Data Availability Statement

Due to the nature of this research, the participants did not agree for their data to be shared publicly. Therefore, no supporting data is available.*

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Associated Data

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Data Availability Statement

Due to the nature of this research, the participants did not agree for their data to be shared publicly. Therefore, no supporting data is available.*

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