Abstract
Background
Oral frailty (OF), a multidimensional decline in oral function, significantly impacts nutrition, quality of life, and overall health, posing heightened risks for older adults recovering from stroke due to neurological impairments and potential dysphagia. Understanding its long-term progression post-stroke remains limited. This longitudinal study aimed to identify distinct oral frailty trajectories and their influencing factors during the critical six-month recovery phase after hospital discharge.
Objective
To investigate the longitudinal trajectory of oral frailty and its influencing factors in older adults with stroke.
Methods
Using convenience sampling, 272 older adults with stroke were recruited from the Department of Neurology between May 2024 and May 2025. The General Information Questionnaire, Oral Frailty Index-8, Oral Health Assessment Scale, Geriatric Oral Health Related Self-Efficacy Scale and Perceived Social Support Scale were used to assess patients within 24 h of admission and at 1, 3, and 6 months after hospital discharge. Oral frailty levels were tracked. Trajectories of change were analyzed using latent variable growth mixed models and influencing factors with logistic regression.
Results
The trajectory of change in oral frailty among older adults with stroke could be categorized into a low-level un-frailty group (35.3%), a medium-level fluctuating group (8.1%), and a high-level persistent frailty group (56.6%); age, number of dentures, history of stroke, dry mouth, subjective masticatory difficulty, level of geriatric oral health-related self-efficacy, and level of comprehended social support were factors influencing the trajectory of change in oral frailty in patients.
Conclusion
Three distinct oral frailty trajectories exist in older stroke adults, showing significant group heterogeneity. Clinical teams should provide trajectory-specific assessments and interventions.
Clinical trial number
Not applicable.
Keywords: Older adults, Stroke, Oral frailty, Latent growth mixture model, Trajectory, Influencing factors, Nursing care
Introduction
Stroke remains a major global public health challenge. According to the World Stroke Organization (WSO), it is the second leading cause of death worldwide, posing a significant burden to global health [1]. This challenge is particularly acute in China against the backdrop of an aging population and changing lifestyles. Epidemiologic data show that there are about 14.94 million existing people with stroke in China, with 3.3 million new cases occurring annually, and about 80% of the survivors are left with varying degrees of functional impairment [2]. It is worth noting that these dysfunctions are not limited to the motor and cognitive levels. Oral-related dysfunctions, especially dysphagia and the resulting deterioration of oral health, are particularly prominent but often overlooked [3]. With the aging of the population, oral diseases among the older adults are becoming increasingly prominent, gradually evolving into a public health issue that seriously affects healthy aging.
Oral frailty (OF) refers to the age-related deterioration of oral health and multidimensional decline of physical and mental functions, encompassing reversible changes like decreased chewing ability and swallowing disorders. It represents a critical transitional stage from oral health to functional decline [4]. Critically, OF and stroke form a vicious cycle [5]. In this cycle, stroke-related impairments reduce social interaction, accelerating functional oral decline and propagating psychosocial barriers to self-care [6, 7]. Crucially, this process may operate specifically through the erosion of oral health self-efficacy (defined as an individual’s confidence in maintaining their oral health), which critically regulates behavioral adherence and mitigates pathological functional decline cycles [8]. Consequently, this progressive erosion of self-efficacy could compound the impact of oral frailty. Studies have shown that oral hypofunction is significantly associated with poor nutritional status [9], which can profoundly impact quality of life. Patients may frequently avoid social gatherings due to eating difficulties, risk falling into social isolation, endure persistent oral pain, and lose interest in life due to dietary restrictions. Ultimately, they might face a dual dilemma: simultaneous decline in both oral function and quality of life.
These significant quality-of-life impacts are especially prevalent among high-risk populations. A Japanese cohort study showed that severe periodontitis increases the risk of oral frailty in patients by 42% [10], and post-stroke neurological oral motor disorders may synergistically amplify this risk, making older adults with stroke an extremely high-risk group for oral frailty prevention and control. Moreover, the presence of OF in stroke survivors has been linked to worse functional recovery and higher care dependency, further diminishing life quality [11]. Therefore, identifying the dynamic trajectories of OF and clarifying its key influencing factors in older adults with stroke is crucial for developing strategies to mitigate its adverse outcomes in this vulnerable population. However, current research predominantly relies on cross-sectional designs [12], which, while valuable for establishing prevalence and associations, are inherently limited in capturing this dynamic evolution over time. As established in epidemiological methodology, this inability to model temporal sequences is a defining limitation of the cross-sectional approach [13].
Furthermore, conventional analyses that assume patient homogeneity mask potential subgroups with distinct trajectories (i.e., heterogeneity). This neglect of heterogeneity obscures critical differences through averaging effects and prevents the prediction of individual outcomes from baseline characteristics. Longitudinal data are central to understanding disease progression and determining optimal intervention timing. In contrast, prospective cohort studies are specifically designed to overcome these limitations by capturing temporal data to elucidate developmental trajectories [13]. To move beyond describing overall group trends and instead uncover distinct subgroups, this study employs Latent Growth Mixture Modeling (LGMM) within a prospective cohort design. Given the scarcity of longitudinal studies applying such advanced modeling to post-stroke OF progression, our aim is to investigate heterogeneity over six months and explore subgroup-specific factors to inform personalized care strategies.
Methods
Subjects of the study
This longitudinal study recruited stroke patients from the Department of Neurology at Binzhou Medical University Hospital between May and July 2024 using convenience sampling. Furthermore, the sample size was determined to meet the requirements for Latent Growth Mixture Model (LGMM) analysis, which recommends a minimum of 200 participants [14]. Furthermore, an a priori calculation was performed using G*Power 3.1 for a repeated-measures ANOVA, with an effect size f = 0.25,α = 0.05, power (1-β) = 0.8, and 4 measurements. The result indicated a required sample size of 159. After accounting for a 20% attrition rate, the minimum sample size was adjusted to 199. To ensure model robustness and account for an anticipated 10–20% attrition rate, 290 participants were enrolled at baseline (T1) from 336 assessed patients after 46 exclusions. Inclusion criteria were: age ≥ 60 years; diagnosis of stroke according to the American Heart Association/American Stroke Association guidelines [15]; inpatient status with clear consciousness and normal communication ability, as defined by a Montreal Cognitive Assessment (MoCA) score ≥ 26; Barthel index ≥ 60 [16]; and provision of informed consent by the patient and/or family members. Exclusion criteria included severe organ dysfunction or a current or history of malignant tumors. The exclusion criteria were applied based on medical record review. During the follow-up period, 18 participants were lost, representing an attrition rate of 6.2%. As detailed in Fig. 1, at the 1-month follow-up (T2), 10 participants were lost (6 withdrawn, 3 refused, 1 with disease exacerbation); at the 3-month follow-up (T3), 6 participants were lost (4 withdrawn, 2 refused); and at the 6-month follow-up (T4), 2 participants were lost due to disease exacerbation. Ultimately, 272 participants completed all follow-up assessments and were included in the final analysis.
Fig. 1.
Flowchart of participant selection and follow-up
Survey tools
General information questionnaire
This questionnaire was designed based on the discussion of the research team and literature review, and is divided into two parts: demographic information and disease-related information. The demographic information includes gender, age, work status, education level, BMI, registered residence, marital status, living status, monthly per capita family income, medical expense payment method, smoking history, and drinking history; the disease-related information includes the type of chronic disease (such as hypertension, diabetes, respiratory diseases, and history of stroke), the number of chronic diseases, multiple medications, the number of teeth, the number of dentures, dry mouth, and subjective chewing difficulties.
Oral frailty index-8 (OFI-8)
This scale was developed by Tanaka et al. [17] and is used to assess the oral frailty of patients. It includes five dimensions, including whether to use dentures, swallowing ability, chewing ability, oral health-related behaviors, and social participation, with a total of 8 items. The total score is 0–11 points, ≥ 4 points indicates oral frailty, and the higher the score, the worse the oral condition. The Cronbach’s α coefficient of this scale is 0.949, and the content validity is 0.934.
Oral health assessment tool (OHAT)
This scale was revised by Australian scholars Chalmers et al. [18] in 2005 based on the Simple Oral Health Checklist. It is mainly used to assess the oral health status of older adults, covering eight oral-related areas, including lips, tongue, gums, dentures, natural teeth, saliva, oral hygiene, and toothache. The total score is 0–16 points, and the higher the score, the worse the oral health status. The Cronbach’s α coefficient of the scale is 0.710, and the test-retest reliability is 0.811.
Geriatric self-efficacy in oral health scale (GSEOH)
This scale was developed by Ohara et al. [8] in 2017. It consists of three dimensions: oral function, oral hygiene habits, and oral clinic habits. It contains 20 items with a total score of 20–80 points. The higher the score, the stronger the oral health-related self-efficacy of older adults. The Cronbach’s α coefficient of this scale is 0.913, and the test-retest reliability is 0.743.
Perceived social support scale (PSSS)
This scale was developed by Zimet et al. [19] and is used to measure an individual’s self-perceived multi-level social support. It includes three dimensions: family support, friend support, and other support, with a total of 12 items. The Likert 7-point scoring method (1–7 points) is used to divide the support level into low, medium, and high, and the total score reflects the individual’s perceived social support level. The Cronbach’s α coefficient of this scale is 0.840.
Data collection and quality control methods
A research team was formed, comprising an attending physician, the head nurse of the Department of Neurology, two supervisor nurses, and one graduate student. All members signed confidentiality agreements prior to participation and completed standardized training, which included specific protocols for data security, patient confidentiality, and the ethical handling of protected health information (PHI). Access to research data was strictly limited to these authorized investigators, who could only access data necessary for the project based on their specific roles. Particular emphasis was placed on the restricted use of WeChat, which was solely for scheduling and distributing links to the secure, password-protected, and encrypted data capture platform; the transmission of any PHI via this application was expressly prohibited. Following successful training, data collection was initiated. To ensure security throughout the data lifecycle, all collected data were de-identified upon entry, with personally identifiable information replaced by a unique study code. The master identification key was stored separately under physical and electronic lock. All research data were subsequently transferred to and stored on encrypted, access-controlled servers maintained by Binzhou Medical University.
The data collection procedure consisted of the following steps. Demographic data were first extracted from medical records within 24 h of admission. Neurologists and research staff subsequently screened for eligible patients who were clinically stable with adequate communication capacity, as objectively confirmed by the MoCA score ≥ 26 inclusion criterion and a neurologist’s clinical assessment. Following the provision of written informed consent from patients and their families, the baseline assessment (T1) was conducted. The T1 assessment involved collecting disease-related data and administering the OFI-8, OHAT, GSEOH, and PSSS scales, with the method of completion (self-report or proxy-report) documented for each. The OFI-8 was selected for follow-up assessments at 1, 3, and 6 months post-discharge because it directly measures the core outcome of oral frailty trajectory. The remaining instruments, which assessed potential influencing factors, were not repeated during follow-up.
To ensure reliability, all scale evaluations were performed independently by two researchers. Prior to assessment, they calibrated their scoring through joint review of the assessment rules and procedures. Any discrepancies in scoring were resolved through consultation with the broader project team. Follow-up assessments (T2-T4) were administered through WeChat, outpatient visits, or telephone contact. At the commencement of each follow-up, the assessment purpose was reiterated. The mode of questionnaire completion (self-report or proxy-report, based on patient capability and preference) was objectively documented by the research staff for all assessments.
Multiple quality control procedures were implemented. To standardize proxy reporting, family members acting as proxies received structured guidance based on an observation guide developed by the research team. They were trained to base their responses on directly observable and measurable behaviors. For instance, for the OFI-8 scale, proxies were instructed to document specific observations such as prolonged meal duration and food avoidance (e.g., consistently refusing hard foods like apples or nuts) for masticatory difficulty; observed coughing during meals for swallowing issues; and a verifiable reduction in social outings for participation frequency. This approach minimized subjective interpretation by anchoring reports in concrete evidence. All evaluations adhered to a unified scoring standard, with all data entries independently verified by two team members before electronic recording.
Statistical methods
Data analysis was conducted using SPSS 26.0 statistical software. Measurement data conforming to normal distribution are expressed as (‾x ± s) and analyzed using analysis of variance; categorical data are presented as case counts and percentages, analyzed by χ² test or Fisher’s exact test; ordinal data were analyzed using the rank-sum test; multinomial logistic regression analysis was adopted to identify influencing factors.
The LGMM analysis was performed using Mplus 8.0 software to identify heterogeneous trajectories of oral frailty in older adults with stroke. P < 0.05 was considered statistically significant.
Latent Class Growth Modeling and Model Selection: Latent Growth Mixture Modeling was implemented to identify distinct trajectories. Models with 1 to 5 latent classes were systematically compared to determine the optimal solution. Model fit was evaluated using the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and sample size-adjusted BIC (aBIC) where smaller values indicate better fit; the Lo-Mendell-Rubin test (LMR) and Bootstrap Likelihood Ratio Test (BLRT) where significant p-values (p < 0.05) suggest superiority of the k-class model over the (k-1)-class model; and Entropy (0–1 scale) which quantifies classification precision with higher values indicating clearer class separation (Entropy > 0.8 suggesting high accuracy).
The model selection integrated statistical fit indices with clinical interpretability. Critically, the 3-class model demonstrated significantly better fit than the 2-class model (LMR P < 0.05, BLRT P < 0.05) with lower AIC, BIC, and aBIC values, along with high classification accuracy (Entropy > 0.8). Moreover, expanding to 4-class and 5-class models yielded non-significant LMR and BLRT results (P > 0.05), indicating no statistical improvement over the 3-class solution. Consequently, the 3-class model was selected as optimal based on its statistical superiority over simpler models, lack of improvement in more complex models and clinically meaningful trajectory patterns.
Results
General information
A total of 272 patients were initially enrolled in this study. During the study, 3 patients withdrew voluntarily, and 4 patients became ill and could not cooperate with the study. Finally, 272 patients completed the study. Among them, there were 158 males and 114 females; aged 60–85 (66.93 ± 5.61) years; 180 unemployed and 92 retired; 126 with primary school education and below, 95 with junior high school education, and 51 with technical secondary school education and above; 238 married and 34 others (unmarried, divorced, widowed); 19 living alone and 253 living with spouse or children; 57 with per capita monthly family income < 3000 yuan, 112 with 3000–5000 yuan, and 103 with > 5000 yuan; 13 with BMI < 18.5 kg/m2, 113 with 18.5 kg/m2 ≤ BMI < 24 kg/m2, and 146 with ≥ 24 kg/m2; 97 with employee medical insurance, 147 with resident medical insurance, and 28 with self-payment.
Latent classes of oral frailty change trajectories in older adults with stroke
Latent class analysis
To identify unobserved subgroups of patients with distinct patterns of oral frailty change over time, we applied latent class analysis. The oral frailty scores of 272 older adults with stroke at T1-T4 were 3.32 ± 0.985, 3.47 ± 1.938, 3.54 ± 1.951, and 3.56 ± 1.992, respectively. The oral frailty level of older adults with stroke in four follow-up assessments was included in the latent class model as an evaluation index, and the model fitting results are shown in Table 1. When divided into 2, 4, or 5 latent classes, the LMR and BLRT results were not significant, indicating that the model fitting effect was poor. The AIC, BIC, and aBIC of the third latent class were smaller than those of the first two latent classes, and Entropy > 0.8. Therefore, the oral frailty change trajectory was divided into three latent classes.
Table 1.
Latent class model fitting results of oral frailty change trajectory in older adults with stroke
| Class No. | AIC | BIC | aBIC | Entropy | LMR(P) | BLRT(P) | Class probability(%) |
|---|---|---|---|---|---|---|---|
| 1 | 4189.615 | 4218.462 | 4193.096 | ||||
| 2 | 2286.822 | 2333.697 | 2292.477 | 0.998 | 0.058 | 0.063 | 35.29/64.71 |
| 3 | 1520.886 | 1585.791 | 1528.717 | 0.999 | 0.005 | 0.006 | 8.09/35.29/56.62 |
| 4 | 1320.677 | 1403.61 | 1330.683 | 0.998 | 0.148 | 0.155 | 3.46/8.09/31.84/56.62 |
| 5 | 896.428 | 997.391 | 908.610 | 0.996 | 0.128 | 0.134 | 3.31/7.35/9.19/31.99/48.16 |
Naming of latent class trajectories
The oral frailty of older adults with stroke was named according to their initial level and change trend, as shown in Fig. 2. Three distinct trajectories were identified: a low-level un-frailty group (C1) (n = 96, 35.3%), which demonstrated a low initial score that decreased and then stabilized with minor fluctuations; a medium-level fluctuation group (C2) (n = 22, 8.1%), characterized by a high initial score that decreased before stabilizing; and a high-level persistent frailty group (C3) (n = 154, 56.6%), which exhibited a high initial score that increased during T1-T2 and continued on a slow upward trend through T2-T4.
Fig. 2.
Latent class trajectory diagram of oral frailty in older adults with stroke
Univariate analysis of the trajectory of oral frailty changes in older adults with stroke
There were no significant differences in the trajectory of oral frailty changes in older adults with stroke with different genders, BMI index, registered permanent residence, average monthly household income, medical expense payment method, drinking history, hypertension history, heart disease history, respiratory disease history, and OHAT scores (P > 0.05). The items with statistically significant differences are shown in Table 2.
Table 2.
Univariate analysis of the trajectory of oral frailty in older adults with stroke
| Variables | n | Low-level un-frailty group(n = 96) | Medium-level fluctuating group(n = 22) | High-level persistent frailty group(n = 154) | χ2/F/Hc | P |
|---|---|---|---|---|---|---|
| Age [years, M(P25, P75)] | 63.0(62.0, 64.0) | 70.0(66.8, 71.3) | 70.0(63, 73.3) | Hc = 72.999 | <0.001 | |
| Work status | χ2=66.907 | <0.001 | ||||
| Unemployed | 180 | 34 | 14 | 132 | ||
| Retired | 92 | 62 | 8 | 22 | ||
| Education level | F = 73.250 | <0.001 | ||||
| Primary school and below | 126 | 14 | 10 | 102 | ||
| Junior high school | 95 | 47 | 8 | 40 | ||
| Technical secondary school and above | 51 | 35 | 4 | 12 | ||
| Marital status | F = 8.019 | 0.014 | ||||
| Married | 238 | 91 | 18 | 129 | ||
| Others (unmarried, divorced, widowed) | 34 | 5 | 4 | 25 | ||
| Living status | F = 6.601 | 0.037 | ||||
| Living alone | 19 | 3 | 4 | 12 | ||
| Living with spouse or children | 253 | 93 | 18 | 142 | ||
| Smoking history | χ2=6.685 | 0.035 | ||||
| No | 137 | 57 | 13 | 67 | ||
| Yes | 135 | 39 | 9 | 87 | ||
| History of diabetes | χ2=9.530 | 0.009 | ||||
| No | 184 | 76 | 15 | 93 | ||
| Yes | 88 | 20 | 7 | 61 | ||
| History of stroke | χ2=134.262 | <0.001 | ||||
| No | 126 | 90 | 5 | 31 | ||
| Yes | 146 | 6 | 17 | 123 | ||
| Number of chronic diseases | F = 33.627 | <0.001 | ||||
| No | 41 | 24 | 2 | 15 | ||
| 1 | 88 | 44 | 6 | 38 | ||
| ≥ 2 | 143 | 28 | 14 | 101 | ||
| Whether multiple medications (≥ 5 ) | F = 10.186 | 0.005 | ||||
| No | 37 | 22 | 2 | 13 | ||
| Yes | 235 | 74 | 20 | 141 | ||
| Number of teeth | F = 72.206 | <0.001 | ||||
| <10 | 88 | 10 | 8 | 70 | ||
| 10–20 | 60 | 11 | 3 | 46 | ||
| >20 | 124 | 75 | 11 | 38 | ||
|
Number of dentures [pieces, M(P25, P75)] |
0.0(0.0, 0.0) | 3.0(0.0, 14.0) | 7.0(2.0,24.0) | Hc = 126.531 | <0.001 | |
| Dry mouth | χ2=33.741 | <0.001 | ||||
| No | 131 | 69 | 9 | 53 | ||
| Yes | 141 | 27 | 13 | 101 | ||
| Subjective chewing difficulty | χ2=16.886 | <0.001 | ||||
| No | 136 | 61 | 4 | 71 | ||
| Yes | 136 | 35 | 18 | 83 | ||
| PSSS[points, M(P25, P75)] | 61.0(58.0, 63.8) | 58.0(54.8, 62.0) | 57.0(52.0, 62.0) | Hc = 20.290 | <0.001 | |
| GSEOH[points, M(P25, P75)] | 56.0(53.3, 57.0) | 49.0(48.0, 52.3) | 45.0(41.0, 49.0) | Hc = 151.157 | <0.001 |
*PSSS is the perceived social support scale, GSEOH is the oral health-related self-efficacy scale for older adults
Multivariable analysis of oral frailty trajectories in older adults with stroke
Variables showing statistical significance in univariate analysis were included as independent variables in a multinomial logistic regression model, with the trajectory of oral frailty as the dependent variable and C1 (low-level un-frailty group) as the reference category. Results indicated that age, number of dentures, PSSS, history of stroke, dry mouth, and subjective chewing difficulty were significant factors for the medium-level fluctuating group (C2). Specifically, age (each 1-year increase: OR = 1.759, P = 0.001) and number of dentures (each additional denture: OR = 2.323, P = 0.043) were risk factors for C2, elevating risk by 75.9% and 2.32-fold, respectively. Protective factors for C2 included higher PSSS scores (OR = 0.855, P = 0.021), absence of stroke history (OR = 0.008, P < 0.001), absence of dry mouth (OR = 0.059, P = 0.012), and absence of subjective chewing difficulty (OR = 0.099, P = 0.035). For the high-level persistent frailty group (C3), significant factors were age, number of dentures, GSEOH, PSSS, history of stroke, and dry mouth. Age (OR = 1.559, P = 0.009) and number of dentures (OR = 2.542, P = 0.024) were risk factors, increasing C3 probability by 55.9% and 2.54-fold per unit increase. Protective factors for C3 included higher GSEOH (OR = 0.607, P < 0.001), higher PSSS (OR = 0.853, P = 0.012), absence of stroke history (OR = 0.027, P = 0.003), and absence of dry mouth (OR = 0.062, P = 0.007) (See Table 3).
Table 3.
Multivariate analysis of the trajectory of oral frailty in older adults with stroke
| Class | Variables | Reference value | β | SE | WaldX² | P | OR | 95% Cl |
|---|---|---|---|---|---|---|---|---|
| C1vs.C2 | Constant term | -9.694 | 15.325 | 0.400 | 0.527 | |||
| Age | 0.565 | 0.177 | 10.139 | 0.001 | 1.759 | 1.243 ~ 2.491 | ||
| Number of dentures | 0.843 | 0.416 | 4.109 | 0.043 | 2.323 | 1.028 ~ 5.249 | ||
| PSSS | -0.157 | 0.068 | 5.305 | 0.021 | 0.855 | 0.748 ~ 0.977 | ||
| History of stroke: No | Yes | -4.849 | 1.363 | 12.666 | <0.001 | 0.008 | 0.001 ~ 0.113 | |
| Dry mouth: No | Yes | -2.831 | 1.130 | 6.279 | 0.012 | 0.059 | 0.006 ~ 0.540 | |
| Subjective chewing difficulty: No | Yes | -2.315 | 1.097 | 4.457 | 0.035 | 0.099 | 0.012 ~ 0.847 | |
| C1vs.C3 | Constant term | 15.414 | 13.773 | 1.252 | 0.263 | |||
| Age | 0.444 | 0.171 | 6.776 | 0.009 | 1.559 | 1.116 ~ 2.178 | ||
| Number of dentures | 0.933 | 0.414 | 5.089 | 0.024 | 2.542 | 1.130 ~ 5.719 | ||
| GSEOH | -0.499 | 0.137 | 13.314 | <0.001 | 0.607 | 0.465 ~ 0.794 | ||
| PSSS | -0.159 | 0.063 | 6.296 | 0.012 | 0.853 | 0.753 ~ 0.966 | ||
| History of stroke: No | Yes | -3.619 | 1.200 | 9.101 | 0.003 | 0.027 | 0.003 ~ 0.281 | |
| Dry mouth: No | Yes | -2.789 | 1.030 | 7.333 | 0.007 | 0.062 | 0.008 ~ 0.463 |
*C1 is the low-level un-frailty group; C2 is the medium-level fluctuating group; C3 is the high-level persistent frailty group. PSSS is the perceived social support scale, GSEOH is the geriatric self-efficacy in oral health scale; age, number of dentures, GSEOH score and PSSS score are input as original values
Discussion
There are three latent categories of oral frailty change trajectories in older adults with stroke
This study used the LGMM model to show that there are three categories of oral frailty change trajectories in older adults with stroke, which are divided into a low-level un-frailty group, a medium-level fluctuation group, and a high-level persistent frailty group, indicating that oral frailty in older adults with stroke has group heterogeneity. The proportion of patients in the high-level persistent frailty group was the highest (56.6%) and remained at a high level, indicating that the progression of stroke was significantly associated with oral frailty, which is similar to the results of Kamide et al. [20]. This may be because some people with stroke have reduced oral hygiene compliance due to motor and cognitive impairment, and swallowing disorders cause increased food residues and microbial load, and multiple risks synergistically accelerate the process of oral frailty [21]. Secondly, patients in the low-level un-frailty group (35.3%) maintained stable and good oral health throughout the follow-up period, which may be attributed to the fact that patients in this group had milder motor and cognitive impairment, stronger self-management ability, better oral health status, and stronger ability to resist disease-related risks. Furthermore, the medium-level fluctuation group (8.1%), though small in size (n = 22), displayed a clinically important pattern of early improvement in oral frailty. This pattern, characterized by a downward trend in the early stage followed by stabilization, potentially reflects the benefit of acute interventions like thrombolytic therapy and early rehabilitation [22]. However, the limited sample size of this subgroup constrains the statistical power to robustly identify the specific factors driving this positive trajectory. Therefore, this finding should be considered preliminary and hypothesis-generating, underscoring the need for future studies with larger samples to confirm the trajectory and elucidate its underlying mechanisms.
Influencing factors of oral frailty change trajectory categories in older adults with stroke
Age
Previous studies have shown [23] that age-related degenerative changes in oral tissues can directly affect oral status. The results of this study showed that compared with the low-level un-frailty group, the older the patient was, the greater the probability of being classified into the medium-level fluctuation group and the high-level persistent frailty group, which is consistent with the results of Ge WY et al. [24]. This may be related to the age-related decline in periodontal ligament cell function, decreased saliva secretion, and dental nerve degeneration. These physiological changes interact with post-stroke cognitive-motor dysfunction and jointly accelerate the process of oral frailty [25, 26]. Therefore, medical staff should use age as an important indicator for stratified intervention and comprehensively implement intervention strategies such as oral hygiene guidance, functional training, nutritional support, and inflammation control to delay the progression of oral frailty and improve the oral health level of patients.
Number of dentures
The presence of dentures is an important indicator of poor oral health [27]. This study found that compared with the low-level un-frailty group, the use of dentures was associated with a higher probability of being classified into the medium-level fluctuation group and the high-level persistent frailty group, which is similar to the results of Nomura et al. [28]. The mechanism may be that dentures cause long-term stimulation and damage to the oral mucosa and remaining teeth, weakening the local repair ability and aggravating the process of oral frailty through systemic inflammatory response. Studies have confirmed that denture biofilms contain a high concentration of Candida albicans, which can easily cause oral inflammation and damage oral health [29]. In addition, discomfort caused by denture wearing can induce the bidirectional effects of social avoidance and psychological stress, leading to insufficient nutritional intake and oral hygiene maintenance, forming a psychological-physiological vicious cycle, and aggravating the progression of oral frailty [30]. Therefore, medical staff should focus on the prevention of oral frailty in older adults with stroke who are denture users, implement comprehensive oral function assessment and management, and provide personalized denture care guidance.
Perceived social support level
The results of this study identified higher perceived social support as a significant protective factor against the progression of oral frailty. Each point increase in PSSS score reduced the odds of belonging to the medium-level fluctuating group (C2) by 14.5% (OR = 0.855, P = 0.021) and the high-level persistent frailty group (C3) by 14.7% (OR = 0.853, P = 0.012) compared to the low-level group (C1). This quantifies the critical role of psychosocial resources in mitigating oral frailty risk post-stroke, likely by enhancing coping capacity and self-care adherence [31]. This improved psychological state likely fosters better engagement in self-care activities, including the maintenance of oral hygiene practices. Conversely, the findings suggest that a deficiency in social support is a risk factor for oral frailty. Older adults with stroke often experience reduced social interaction due to physical limitations and cognitive deficits. As noted in other studies [32], this social isolation can lead to diminished chewing opportunities and weakened oral muscle function, ultimately hindering the acquisition and application of crucial oral health knowledge. Therefore, it is imperative for healthcare professionals to recognize the vital role of social support in preserving oral health among older adults with stroke. Clinical strategies should focus on actively fostering psychosocial support systems, encouraging participation in community or group-based activities to combat isolation, and delivering targeted oral health education and functional training within this supportive context.
History of stroke
The recurrence rate 1 year after stroke is 17.1% [33]. The results of this study showed that compared with the low-level un-frailty group, those with a history of stroke were more likely to be classified into the medium-level fluctuation group and the high-level persistent frailty group, which is similar to the results of Hironaka et al. [7]. The reason may be that patients with a history of stroke are usually older and have more significant symptoms and neurological damage when they suffer a recurrent stroke [34]. Kant et al. [35] showed that recurrent stroke is often accompanied by elevated systemic inflammatory markers and metabolic disorders, which accelerate atherosclerosis and aggravate the debilitating state. At the same time, bacterial infection caused by oral inflammation can accelerate atherosclerosis and thrombosis, affecting the cardiovascular and cerebrovascular systems, forming a vicious cycle between oral problems and stroke [36]. Therefore, medical staff should strengthen oral health management for older adults with recurrent stroke, regularly monitor inflammatory markers and metabolic status, and develop personalized intervention strategies to delay the progression of atherosclerosis, break the vicious cycle, and improve patient prognosis.
Dry mouth
Dry mouth is one of the core symptoms of oral frailty and is more common among older adults [37]. The results of this study showed that patients with dry mouth symptoms were more likely to be classified into the medium-level fluctuation group and the high-level persistent frailty group compared with the low-level un-frailty group, which is consistent with the study of Hu S et al. [38]. Dry mouth symptoms are mainly caused by reduced saliva secretion, which directly impairs the oral self-cleaning function, leading to oral problems such as food residue retention, plaque accumulation, caries and periodontal disease. Dry mouth can also indirectly affect oral health by affecting chewing and swallowing functions, limiting the patient’s nutritional intake [39]. In addition, people with stroke often suffer from decreased salivary gland secretion function due to neurological damage. Long-term use of anticholinergic drugs (such as antihypertensive drugs commonly used after stroke) further inhibits the parasympathetic nervous system regulation of the salivary glands, resulting in salivary secretion disorders under the dual effects of “nerve-drug”, aggravating dry mouth symptoms and accelerating the process of oral deterioration [40]. Therefore, medical staff should attach great importance to the early screening and intervention of dry mouth symptoms in older adults with stroke. Dry mouth symptoms can be relieved through artificial saliva replacement therapy, drug stimulation of saliva secretion and oral moisturizing care. At the same time, oral health education and functional training should be combined to improve patients’ oral hygiene behavior, so as to delay the process of oral deterioration and improve patients’ quality of life.
Subjective chewing difficulty
Subjective chewing difficulty is an important indicator for assessing oral dysfunction and systemic health risks [41]. This study found that older adults with stroke who reported subjective chewing difficulty were more likely to be classified into the medium-level fluctuation group than those in the low-level un-frailty group. From a mechanistic perspective, subjective chewing difficulty means that patients face obstacles in the process of chewing food, which reduces chewing efficiency and affects the effective intake of nutrients. Over time, the patient’s nutritional status deteriorates, the body’s immunity decreases, and the repair and regeneration ability of oral tissues is weakened, affecting oral health. At the same time, due to insufficient chewing frequency and strength, oral muscles atrophy and weaken, further damaging the normal functional structure of the oral cavity. Studies have shown [42] that long-term chewing difficulties can cause patients to have negative emotions such as anxiety and depression. These adverse psychological states can interfere with the patient’s daily routine, leading to a weak awareness of oral hygiene maintenance, a reduced frequency of oral cleaning behavior, and the growth of bacteria in the oral cavity, increasing the risk of oral infection and worsening the degree of oral frailty. In view of this, medical staff should attach great importance to the subjective chewing difficulty of older adults with stroke, regard it as an important entry point for the prevention of oral frailty, and conduct a comprehensive oral function assessment on patients with subjective chewing difficulty, including masticatory muscle strength test, dental condition examination, etc., and formulate personalized intervention plans based on the assessment results, such as guiding patients to conduct chewing function training, while strengthening psychological counseling to help patients improve their mental state and enhance their compliance with oral hygiene maintenance, thereby effectively preventing the occurrence and development of oral frailty.
Geriatric self-efficacy in oral health
Oral health-related self-efficacy is an important indicator of older adults’ ability to maintain their own oral health. The results of this study showed that the lower the oral health-related self-efficacy, the higher the probability of being classified into the high-level persistent frailty group compared with the low-level un-frailty group. The reason may be that older adults with low self-efficacy often lack confidence in their ability to perform oral hygiene behaviors, resulting in a decrease in the frequency and quality of daily oral hygiene behaviors. Moreover, when faced with oral health problems, these patients are less willing to actively seek professional help, which delays early treatment and causes oral problems to become more serious [43]. In addition, from a psychological perspective, low self-efficacy may also trigger negative emotions such as anxiety and helplessness, affecting patients’ motivation to maintain good oral health habits. Studies have shown [44] that older adults with high self-efficacy are more likely to perform oral hygiene regularly, actively seek oral medical services, and effectively deal with oral problems. Therefore, medical staff should list patients with low self-efficacy as the key population for oral frailty prevention. Through structured health education and peer support intervention, the self-efficacy of older adults with stroke can be improved, thereby breaking the vicious cycle of “lack of cognition-negative behavior-oral deterioration” and providing behavioral intervention targets for oral frailty prevention and control.
Implications for nursing practice and research
This trajectory-based framework provides a structured approach for managing oral frailty in stroke patients by integrating stratified screening and targeted interventions into routine nursing care. We recommend implementing universal admission screening using a tool such as the Oral Frailty Index-8 (OFI-8) to enable early trajectory stratification. Nursing interventions should then be precisely tailored: patients in the low-level un-frailty group (C1) should receive preventive education and periodic monitoring to maintain stable oral function; those in the medium-level fluctuating group (C2) require dynamic post-discharge assessments to identify periods of functional decline, during which rehabilitative treatments such as masticatory training should be actively initiated; while patients in the high-level persistent frailty group (C3) need immediate, nurse-coordinated interdisciplinary care involving dentists, rehabilitation physicians, and nutritionists to address complex issues like xerostomia management and adaptive feeding strategies. By translating these trajectory patterns into differentiated care plans, nursing professionals can transition from standardized protocols to precision interventions that effectively address the specific needs of each patient subgroup.
Conclusion
Based on longitudinal latent growth mixture modeling, this study identified three distinct oral frailty trajectories in older adults with stroke: low-level stable, medium-level fluctuating, and high-level persistent, revealing significant heterogeneity in functional decline patterns. Clinical care teams should implement precision predictive interventions through dynamic risk profiling, prioritizing high-risk subgroups (e.g., advanced age, denture dependence, or stroke history) for ongoing monitoring. For fluctuating trajectories, focus should center on masticatory rehabilitation to disrupt functional instability; for persistent decline, integrate self-efficacy coaching and neuro-oral comorbidity management. Future research must establish tiered dynamic assessment systems aligning interventions with trajectory-specific vulnerabilities to proactively address evolving oral health needs.
Limitations
This study has several limitations. First, the single-center design may limit the generalizability of our findings. Variations in patient populations, stroke management protocols, and healthcare systems across different regions could affect the external validity of the trajectory models. Second, to ensure data quality, we excluded individuals with significant cognitive or mental impairments. While methodologically necessary, this limits the representation of a subgroup particularly vulnerable to oral frailty. Future research should specifically target this population to develop tailored interventions. Third, although ethical safeguards were implemented, recruiting patients in the acute phase (< 24 h post-stroke) warrants cautious interpretation of informed consent due to the inherent vulnerability of this population. Future multi-center studies are needed to validate our models and explore feasibly extending the recruitment window without compromising baseline assessment. Fourth, the small sample size of the medium-level fluctuating group precluded direct comparisons with other trajectories, and along with potential residual confounding from unmeasured factors, suggests that our findings should be validated in larger, future studies.
Author contributions
J.Z. and H.X. proposed the study idea. J.Z., Y.L. and S.L. collected and analyzed the data. J.Z. and Q.Z. plotted the figures. M.Z. and H.S. plotted the tables. J.Z. drafted the manuscript. H.X. critically revised the manuscript. All authors contributed to the article and approved the submitted version.
Funding
This study did not receive any financial support.
Data availability
The data that support the findings of this study are available from the corresponding author on reasonable request.
Declarations
Ethics approval and consent to participate
The study protocol complied with the principles of the Declaration of Helsinki. The study was approved by the Ethics Committee of Binzhou Medical University (No. 2024-L031). After being informed of the purpose and significance of the study, all participants signed a written informed consent form.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Jiayue Zhao, Yujia Liu and Shuqin Liang are share first authorship.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data that support the findings of this study are available from the corresponding author on reasonable request.