Classification and influencing factors of fear of falling in elderly stroke patients: A latent profile analysis

Abstract

Objective

To explore the potential categories of fear of falling in elderly stroke patients and analyze the differences in characteristics and influencing factors among patients in different categories.

Methods

AA total of 386 elderly stroke patients hospitalized in the Department of Neurology of a tertiary grade A general hospital in Jilin Province from March 2024 to June 2024 were selected as research subjects using the convenience sampling method. A general information questionnaire, Modified Falls Efficacy Scale (MFES), Simplified Coping Style Questionnaire (SCSQ), and Social Support Rating Scale (SSRS) were used for the survey. Mplus 8.3 software was applied to conduct latent profile analysis (LPA) on fear of falling in elderly stroke patients to identify potential categories, and multivariate logistic regression was used to further explore the influencing factors of each category.

Results

There were 3 potential categories of fear of falling in elderly stroke patients: the high fear of falling group (21.8%), moderate fear of falling group (38.3%), and low fear of falling group (39.9%). Multivariate logistic regression analysis showed that gender, age, type of stroke diagnosis, visual status, hearing status, limb strength, coping style, and social support were the influencing factors for the potential categories of fear of falling in elderly stroke patients.

Conclusion

Fear of falling in elderly stroke patients has obvious categorical characteristics. Medical staff should implement targeted interventions based on the characteristics and influencing factors of different potential categories to reduce patients’ fear of falling.

Introduction

With the continuous development of the economy and the intensification of aging, the number of elderly people in China and their proportion in the total population have been increasing, gradually becoming an important social issue [1]. According to the data released by the National Bureau of Statistics in the “Seventh National Population Census Bulletin (No. 5)” in 2021 [2], the population aged 60 and above in China has reached 264 million, accounting for 18.7% of the total population. Stroke, also known as apoplexy, is a cerebrovascular disease caused by sudden cerebrovascular damage [3]. The World Stroke Organization (WSO) mentioned in the 2022 Global Stroke Report that stroke is the main cause of death and disability in Chinese adults [4]. According to the “China Stroke Prevention and Treatment Report 2021”, the number of stroke patients in China ranks first in the world [5], with the main affected population being the elderly. Moreover, the risk of stroke recurrence increases with age [6]. Studies have shown that the incidence and severity of fear of falling in stroke patients are significantly higher than those in non-stroke patients [7]. The development of fear of falling is related to aging and is common in the elderly population [8]. Weakened lower limb muscle strength, balance dysfunction, and limited joint movement make them more prone to falling. It is reported that the incidence of falls in elderly stroke patients in the first year after stroke ranges from 40.48% to 73% [9, 10], and the risk of falling exists in all stages of stroke occurrence and development [11]. Falls not only cause serious physical injuries and affect self-care ability, which is not conducive to early rehabilitation, but also reduce patients’ quality of life and even lead to negative psychological states [12]. Previous studies on fear of falling and its influencing factors in elderly stroke patients have focused on the entire population [13, 14], ignoring the internal differences within the group. This leads to a lack of targeting in clinical individualized practice, resulting in unsatisfactory intervention effects and waste of medical resources. Latent profile analysis (LPA) is an individual-centered method that groups homogeneous individuals based on continuous data, classifying groups with similar symptoms into the same category to further analyze the characteristics of each group [15]. Therefore, this study uses LPA to explore the potential categories and characteristics of fear of falling in elderly stroke patients and analyze their influencing factors, aiming to provide a reference for clinicians to formulate targeted intervention measures.

Subjects and methods

Study subjects

Elderly stroke patients hospitalized in the Department of Neurology of a tertiary grade A general hospital in Jilin Province from March 2024 to June 2024 were selected using convenience sampling. Inclusion criteria: (1) Meeting the diagnostic criteria for stroke in the Diagnostic Criteria for Major Cerebrovascular Diseases in China 2019 formulated by the Cerebrovascular Disease Group of the Neurology Branch of the Chinese Medical Association [16], confirmed by CT or MRI; (2) Aged ≥ 60 years; (3) Clear consciousness, able to understand and answer questions; (4) Informed consent and voluntary participation in the study. Exclusion criteria: (1) Poor disease progression, unable to cooperate; (2) Complicated with other critical diseases; (3) With mental disorders; (4) With sequelae such as aphasia, reading/writing disorders, or comprehension barriers that prevent cooperation. According to Methods in Nursing Research [17], the sample size was determined as 10 times the number of selected variables. Considering the loss of questionnaires or invalid questionnaires, the sample size was expanded by 20%. There were 25 variables in this study, so the initial sample size was 250 cases. After expanding by 20%, the final sample size was 300 cases, and the actual sample size in this study was 386 cases.

Measuring tool

General information questionnaire

Self-designed by the researchers based on literature review and expert opinions, including: (1) Sociodemographic information: gender, age, education level, residence, marital status, smoking, drinking, etc.; (2) Disease-related factors: visual status, hearing status, limb strength, stroke type, etc.; (3) Activity-related factors: use of assistive devices, fall history, etc.; (4) Living conditions: sleep, etc.

Modified falls efficacy scale (MFES)

Revised by Hill et al. [18] in 1996 based on the FES, it is used to assess the fall efficacy of subjects. The scale evaluates fear of falling and its severity through subjects’ performance in designated activities, including two dimensions: daily indoor activities and outdoor activities, with 14 items. Each item is scored from 0 to 10, where 0 indicates no confidence at all and 10 indicates extreme confidence. Higher scores indicate lower fear of falling. The internal consistency Cronbach’s α of the scale is 0.95, the test-retest reliability is 0.93, and the construct validity is good. The Cronbach’s α coefficient in this study was 0.988.

Social support rating scale (SSRS)

Compiled by Xiao Shuiyuan [19] in 1986 based on foreign scales and combined with domestic conditions, it assesses individual social support from three aspects: objective social support, subjective social support, and utilization of social support, including 10 items. Items 1–4 and 8–10: each item is scored 1–4 according to the options 1–4. Item 5: scored by summing the scores of subitems A-D, with 1–4 points for “no support” to “full support”. Items 6–7: scored 0 if “no source” is selected, and the number of sources if “the following sources” are selected. The total score is the sum of all items, with higher scores indicating higher social support. The total Cronbach’s α coefficient of the scale is 0.920, and the Cronbach’s α coefficients of each item range from 0.89 to 0.94. It is concise and effective, widely used in domestic social support research. The Cronbach’s α coefficient in this study was 0.896.

Simplified coping style questionnaire (SCSQ)

Compiled by Xie Yaning [20] based on domestic and foreign coping style theories and the characteristics of Chinese population, it is concise and easy to use. The scale includes two dimensions: positive coping (12 items, items 1–12) and negative coping (10 items, items 13–20). All items are scored on a 4-point Likert scale (0 = never used, 1 = occasionally used, 2 = sometimes used, 3 = frequently used). Higher scores in the positive coping dimension indicate more positive coping styles, and higher scores in the negative coping dimension indicate more negative coping styles. The total Cronbach’s α coefficient of the scale is 0.90, with 0.89 for positive coping and 0.78 for negative coping, showing good reliability and validity. In this study, the Cronbach’s α coefficients were 0.838 for positive coping and 0.778 for negative coping.

Data collection and quality control

After obtaining approval from the hospital’s nursing department and relevant departments, investigators were strictly trained. Trained investigators selected subjects according to inclusion and exclusion criteria, explained the purpose and significance of the survey, and obtained their cooperation. Subjects filled out the questionnaires independently without interference. For subjects with visual impairment or low education level, investigators read the questionnaire items in neutral language and assisted them in completing the forms. Investigators checked the completeness and authenticity of the questionnaires when collecting them. After data collection, double-entry method was used for verification and error correction. If data inconsistencies occurred, the original questionnaires were reviewed to minimize human errors.

Statistical methods

Mplus 8.7 and SPSS 26.0 software were used for latent profile model construction and data analysis. Specific steps: 1) Mplus 8.7 was used to perform latent profile analysis on fear of falling and establish latent profile models. Three types of fitting indices were used for evaluation: ① Information indices: Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and adjusted BIC (aBIC); ② Classification index: Entropy, ranging from 0 to 1, to judge classification accuracy; ③ Likelihood ratio test indices: Lo-Mendell-Rubin likelihood ratio test (LMR) and Bootstrap likelihood ratio test (BLRT). The criteria for the optimal model were: minimum AIC, BIC, and aBIC; Entropy ≥ 0.8 (closer to 1 indicates more accurate classification); statistically significant LMR and BLRT (P < 0.05); and each category probability ≥ 0.05. A significant P-value (P < 0.05) indicates that the k-class model fits better than the (k-1)-class model. After determining the latent profiles of fear of falling in stroke patients, normally distributed quantitative data were expressed as mean ± standard deviation, and qualitative data were described as frequency and percentage (%). Chi-square test or one-way ANOVA was used to screen indices with statistically significant differences among groups, and multivariate logistic regression was used to further analyze the influencing factors of each profile.

Ethics statement

This study complies with the Declaration of Helsinki and has been approved by the Ethics Committee (Ethics No.: 10254, Ethics Review Committee of the Medical College of Y University).

Results

General information of subjects

A total of 386 elderly stroke patients were surveyed, including 57.5% males and 42.5% females; 51% aged 60–69 years and 49% aged ≥ 70 years; 42.2% with education level of primary school or below and 57.8% with junior high school or above; 49% living in rural areas and 51% in urban areas; 14.5% living alone and 85.5% living with others; 78% married and 22% unmarried; 33.9% with fall history and 66.1% without; 17.6% smokers and 82.4% non-smokers; 32.4% drinkers and 67.6% non-drinkers; 64.8% with ischemic stroke and 35.2% with hemorrhagic stroke; 80.1% with normal vision and 19.9% with impaired vision; 75.6% with normal hearing and 24.4% with impaired hearing; 62.7% with normal limb strength and 37.3% with weakened limb strength; 25.1% using assistive devices and 79.4% not using; 45.1% with good sleep and 54.9% with poor sleep (see Table 1 and Fig. 1).

Table 1.

Univariate analysis of potential categories of influences on fear of falling in elderly stroke patients (n = 386)

Variables	Values	High fall Fear Group (n = 84)	Moderate fear of falling group (n = 148)	Low fall Fear Group (n = 154)	χ2/F	P
Gender	Male	45 (53.6)	71 (48.0)	106 (68.8)	14.120	< 0.001
	Female	39 (46.4)	77 (52.0)	48 (31.2)
Age	60–69 years	16 (19.0)	67 (45.3)	114 (74.0)	68.937	< 0.001
	≥ 70 years	68 (81.0)	81 (54.7)	40 (26.0)
Education level	Primary school or below	46 (54.8)	78 (52.7)	39 (25.3)	30.102	< 0.001
	Junior high school or above	38 (45.2)	70 (47.3)	115 (74.7)
Residence	Rural	47 (56.0)	81 (54.7)	61 (39.6)	9.002	0.011
	Urban	37 (44.0)	67 (45.3)	93 (60.4)
Marital status	Married	59 (70.2)	112 (75.7)	130 (84.4)	7.104	0.029
	Unmarried	25 (29.8)	36 (24.3)	24 (15.6)
Fall history	Yes	46 (54.8)	53 (35.8)	32 (20.8)	28.372	< 0.001
	No	38 (45.2)	95 (64.2)	122 (79.2)
Smoking	Yes	15 (17.9)	19 (12.8)	34 (22.1)	4.444	0.108
	No	69 (82.1)	129 (87.2)	120 (77.9)
Drinking	Yes	21 (25.0)	41 (27.7)	63 (40.9)	8.684	0.013
	No	63 (75.0)	107 (72.3)	91 (59.1)
Stroke type	Ischemic	45 (53.6)	96 (64.9)	109 (70.8)	7.054	0.029
	Hemorrhagic	39 (46.4)	52 (35.1)	45 (29.2)
Visual status	Normal	51 (60.7)	115 (77.7)	143 (92.9)	35.995	< 0.001
	Impaired	33 (39.3)	33 (22.3)	11 (7.1)
Hearing status	Normal	42 (50.0)	111 (75.0)	139 (90.3)	47.877	< 0.001
	Impaired	42 (50.0)	37 (25.0)	15 (9.7)
Limb strength	Normal	20 (23.8)	92 (62.2)	130 (84.4)	85.389	< 0.001
	Weakened	64 (76.2)	56 (37.8)	24 (15.6)
Assistive device use	Yes	64 (76.2)	29 (19.6)	4 (2.6)	160.369	< 0.001
	No	20 (23.8)	119 (80.4)	150 (97.4)
Sleep status	Good	28 (33.3)	58 (39.2)	88 (57.1)	15.807	< 0.001
	Poor	56 (66.7)	90 (60.8)	66 (42.9)
Positive coping score (points)		25.21 ± 4.47	29.57 ± 4.77	33.08 ± 5.35	69.625	< 0.001
Negative coping score (points)		19.31 ± 4.48	14.92 ± 4.64	12.90 ± 2.50	74.069	< 0.001
Social support score (points)		27.35 ± 8.82	33.54 ± 9.85	42.2 ± 9.87	70.217	< 0.001

Fig. 1.

Distribution characteristics of potential categories of fear of falling in elderly stroke patients

Results of latent profile analysis on fear of falling in elderly stroke patients

Using the 14 items of the MFES as manifest indicators, 1–4 latent profile models were fitted to explore the latent profiles of fear of falling in elderly stroke patients (Table 2). Starting from the initial model, this study sequentially fitted profile models containing 1 to 4 latent categories. As the number of latent categories increased, AIC, BIC, and aBIC all exhibited a gradual downward trend, indicating an optimal balance between model complexity and data fit, with model fitting performance progressively improving. Regarding classification accuracy, Entropy reached its peak in the 2-category model, indicating the lowest uncertainty in individual classification under this model. However, when the number of categories increased to 4, LMRT results were no longer significant (P > 0.05), suggesting that further increasing the number of categories did not significantly improve model fit, and model adaptability was not supported. Furthermore, LMR results show that the 2-category model significantly outperforms the 1-category model, and the 3-category model significantly outperforms the 2-category model, supporting the statistical validity of expanding to three categories. Although the 3-category model exhibits the lowest Entropy value among the four candidate models, its value remains above 0.8, indicating it still possesses good classification accuracy and clear category discrimination capability. More importantly, the three-category model theoretically offers superior interpretability, enabling clearer and more explicit presentation of heterogeneous characteristics in fall fear among different stroke patient categories. This facilitates the identification of subgroups with distinct psychological and behavioral traits. Balancing statistical metrics and clinical interpretability, this study ultimately selected the three-category model as the optimal latent profile model. According to the classification of fear of falling, the profiles were named as high fear of falling group (21.8%), moderate fear of falling group (38.3%), and low fear of falling group (39.9%).

Table 2.

Fitting indices of latent profile models for fear of falling in elderly stroke patients (n = 386)

Model	AIC	BIC	IBC	Entropy	P		Class Probability
					LMRT	BLRT
1	24571.587	24682.35	24593.51
2	19448.927	19619.028	19482.594	0.998	< 0.001	< 0.001	0.25130/0.74870
3	17766.034	17995.472	17811.445	0.961	0.0267	< 0.001	0.21762/0.38342/0.39896
4	16704.100	16992.876	16761.255	0.972	0.2301	< 0.001	0.19171/0.19171/0.46114/0.15544

Univariate analysis of influencing factors for latent categories of fear of falling

There were no statistically significant differences in the latent profiles of fear of falling among patients with different living styles, family monthly income per capita, or smoking status (all P > 0.05). Items with statistically significant differences are shown in Table 1. There were statistically significant differences in scores of positive coping, negative coping, and social support among the three profiles (all P < 0.001).

Multivariate logistic regression analysis of influencing factors for latent categories of fear of falling

Variables with statistical significance in univariate analysis were included as independent variables, and the latent categories of fear of falling were used as dependent variables for multivariate logistic regression analysis. Assignment of independent variables: gender (female = 0, male = 1); age (≥ 70 years = 0, 60–69 years = 1); stroke type (hemorrhagic = 0, ischemic = 1); visual status (impaired = 1, normal = 2); hearing status (impaired = 1, normal = 2); limb strength (weakened = 1, normal = 2); negative coping score and social support score were entered as original values. The results of multivariate logistic regression analysis are shown in Table 3.

Table 3.

Multiple logistic regression analysis of potential categories of fear of falling in elderly stroke patients(n = 386)

Variables	Classification	Moderate Fear of Falling Group			High Fear of Falling Group
		OR	95% CI	P	OR	95% CI	P
Gender	Male
	Female	2.679	[1.184,6.060]	0.018	2.979	[1.687,5.264]	< 0.001
Age (years)	60–69
	≥ 70	3.826	[1.566,9.352]	0.003	2.052	[1.151,3.660]	0.015
Stroke type	Ischemic
	Hemorrhagic	5.675	[2.428,13.264]	< 0.001	1.803	[1.017,3.198]	0.044
Visual status	Normal
	Impaired	2.959	[1.047,8.368]	0.041	2.074	[0.900,4.777]	0.087
Hearing status	Normal
	Impaired	7.161	[2.751,18.642]	< 0.001	2.746	[1.281,5.886]	0.009
Limb strength	Normal
	Weakened	11.382	[4.814,26.911]	< 0.001	2.530	[1.342,4.770]	0.004
Negative coping score	-	1.262	[1.122,1.420]	< 0.001	1.077	[0.976,1.188]	0.141
Social support total score	-	0.912	[0.867,0.960]	< 0.001	0.936	[0.904,0.969]	< 0.001

Note: The low fear of falling group was used as the reference group

Discussion

Potential categories of fear of falling in elderly stroke patients

This study employed latent profile analysis and, based on various model fit indices, identified distinct classification characteristics in fall fear among elderly stroke patients, indicating significant heterogeneity within this population. Specifically, participants could be categorized into three latent groups: high fall fear, moderate fall fear, and low fall fear. Among these, the high fear of falling group constituted the smallest proportion, the low fear of falling group the largest, and the moderate fear of falling group a slightly smaller but still substantial proportion. Results indicate that overall, elderly stroke patients exhibit moderate levels of fear of falling, consistent with findings from multiple previous studies [13, 21, 22]. This classification holds not only statistical significance but also important implications for clinical practice.

From a clinical management perspective, patients with different levels of fall fear require differentiated assessment and intervention strategies. For the high fall fear group, who often exhibit significant activity avoidance and anxiety/depression, rehabilitation should prioritize psychological-behavioral interventions and safety support. Specific measures may include: providing cognitive behavioral therapy (CBT) to correct catastrophic thinking; implementing graded exposure training to gradually rebuild confidence in activities; combining balance and muscle strength training to enhance physical capabilities; and ensuring comprehensive safety measures in the rehabilitation environment (e.g., using protective gear, home modifications) to alleviate psychological burdens. For the moderate fall fear group, patients exhibit some fear but retain partial willingness to engage in activities. Interventions should primarily focus on education, motivation, and functional exercise. Health education can enhance their awareness of fall risks, teach fall prevention strategies (e.g., using walkers, avoiding high-risk movements), and design individualized, progressive rehabilitation exercise plans. These plans should improve activity endurance and balance function while ensuring safety, preventing further escalation of fear. For the low fall fear group, despite minimal fall anxiety, prevention and health promotion remain crucial. Interventions should focus on maintaining and enhancing existing functional abilities. This can be achieved through sustained physical activities (e.g., Tai Chi, balance training), regular follow-up assessments, and the development of community support networks to prevent escalating fear or actual falls.

Fear of falling often constitutes an unseen burden for elderly stroke patients. This psychological state may lead to reduced physical activity and social avoidance, thereby exacerbating isolation and negative living conditions [23]. It not only directly increases the risk of anxiety and depression [24] but may also trigger further declines in muscle strength and balance function due to decreased activity, forming a vicious cycle of “fear-inactivity-frailty” that ultimately worsens functional impairment [25]. Therefore, during stroke rehabilitation, alongside focusing on physical functional recovery, psychological assessment of fall fear should be integrated into routine management. Tiered interventions tailored to different categories should be implemented to effectively alleviate patients’ excessive concerns, enhance their confidence in activities, and improve quality of life, thereby achieving the goal of integrated physical and psychological rehabilitation.

Influencing factors of latent categories of fear of falling

The results showed that gender, age, stroke type, visual status, hearing status, limb strength, negative coping, and social support were influencing factors of fear of falling in elderly stroke patients. Females were more likely to experience fear of falling than males, consistent with the study by Hussain et al. [26], which may be attributed to physiological and psychological differences between genders. Patients with hemorrhagic stroke had a higher risk of fear of falling than those with ischemic stroke, possibly due to differences in pathogenesis, characteristics, and risk factors, which require further exploration. Elderly stroke patients aged ≥ 70 years or with impaired vision/hearing were more prone to fear of falling. A cross-sectional study indicated that age is an influencing factor of fear of falling in the elderly, and advanced age and visual impairment are significantly associated with fear of falling in the elderly [27]. With increasing age, patients’ physiological functions gradually decline, especially balance ability, often accompanied by weakened hearing and vision, leading to reduced limb coordination and environmental perception, thus increasing the risk of falling and fear of falling in stroke patients during activities [28]. Patients with weakened limb strength were more likely to have fear of falling. Studies have shown that limb strength is the most important influencing factor of fear of falling related to daily activities in stroke patients [29]. Weakened limb strength limits patients’ activity ability, reduces gait speed and endurance, makes independent walking and posture changes difficult, and thus increases fear of falling. Low social support was associated with a higher risk of fear of falling, consistent with previous studies [30]. Good social support can positively affect patients’ health behaviors, physical activity, self-efficacy, and mental health, which directly influence the level of fear of falling and fall risk [28]. Negative coping styles were more likely to lead to fear of falling, consistent with previous studies [31], possibly because patients using negative coping styles lack confidence in disease rehabilitation and life, resulting in negative emotions such as fear of falling.

Conclusion

This study revealed the heterogeneity of fear of falling in elderly stroke patients, which can be divided into three potential categories: high, moderate, and low fear of falling groups. The influencing factors include gender, age, stroke type, visual status, hearing status, limb strength, negative coping, and social support. Medical staff should implement more precise and targeted interventions based on the characteristics and influencing factors of different potential categories to effectively reduce patients’ fear of falling.

Limitations

First, as a cross-sectional study, its findings only reflect conditions at a specific point in time and cannot establish causal relationships between variables. Additionally, this study did not address controlling for differences in rehabilitation interventions among patients. For instance, varying rehabilitation strategies—such as physical therapy, balance training, and psychological support—may exert distinct effects on fear of falling. Future research could further evaluate the relationship between different rehabilitation programs and fear of falling. On the other hand, while this study emphasizes the importance of personalized interventions, it has not proposed specific, actionable intervention protocols or quantitative assessment methods. For instance, how to design multidimensional measures combining psychosomatic interventions, behavioral therapy, environmental adaptations, and family support for different subgroups, and how to quantify intervention effects through clinical indicators or patient-reported outcomes (PROs), remain areas requiring further exploration. Furthermore, the relatively limited sample size and single source of participants may affect the representativeness and generalizability of the findings. Future studies should combine qualitative interviews with longitudinal follow-up designs, expand sample size and coverage, and conduct multi-level investigations into the trajectory and influencing factors of fall fear among elderly stroke patients. Based on these findings, structured intervention protocols tailored to different potential categories should be developed, implemented, and evaluated to enhance the practical guidance value of the research.

Author contributions

All authors contributed to study design, data collection, data analysis and interpretation, and writing and approval of the manuscript. Drafting the article and interpretation of analysis results: Yue Yang; data acquisition: Xiaoge Ding and Lei Zhang; data analysis: Meiying Li and Yue Yang; manuscript preparation and revision: Yue Yang and Yinji Jin; conception and design of the study, interpretation of analysis results and final approval of the version to be submitted: Meixiang Jin and Yinji Jin.

Funding

This study was supported by grants from the Education Department of Jilin Province (JJKH20240707KJ).

Data availability

No datasets were generated or analysed during the current study.

Declarations

Competing interests

The authors declare no competing interests.

Ethics approval

This study complies with the Declaration of Helsinki and has been approved by the Ethics Committee (Ethics No.: 10254, Ethics Review Committee of the Medical College of Yanbian University).

Informed consent

All participants provided informed consent prior to their participation.