Skip to content
Frontiers
Search

Original Research

Frontiers in Public Health ·

Volume 14 - 2026 | https://doi.org/10.3389/fpubh.2026.1905197

Latent class analysis of intrinsic capacity and its association with quality of life in hospitalized older people with stroke: a cross-sectional study

  • Xin-Xin Tang1
  • Qian-jie Wu2
  • Xiao-Min Zhang1
  • Qiu-Yue Tang1
  • Ling Yu1*corresponding author
  1. 1Department of Neurology, The First Affiliated Hospital with Nanjing Medical University, Nanjing, Jiangsu, China
  2. 2Department of Nursing, The Affiliated Suqian First People's Hospital of Nanjing Medical University, Suqian, Jiangsu, China
Article metrics0 viewsView details
PDFCiteMetrics

Abstract

This study aimed to identify latent classes of intrinsic capacity among hospitalized older people with stroke and to examine the associations of these classes with stroke-specific quality of life. A cross-sectional design was used, and 314 hospitalized older people with stroke were consecutively recruited. Stroke severity was assessed using the National Institutes of Health Stroke Scale (NIHSS), comorbidity burden using the Age-adjusted Charlson Comorbidity Index (ACCI), perceived social support using the Perceived Social Support Scale (PSSS), intrinsic capacity across five domains using the Intrinsic Capacity Assessment Tool, and stroke-specific quality of life using the Stroke-Specific Quality of Life Scale (SS-QOL). Latent class analysis identified intrinsic-capacity profiles, and multivariable logistic regression was used to examine factors associated with latent-class membership, and ANOVA was used to compare SS-QOL scores across the identified classes. LCA identified three latent classes of intrinsic capacity among hospitalized older people with stroke: the low motor–low vitality group (27.70%), the low cognitive–moderate vitality, psychological, and sensory group (17.20%), and the function maintenance group (55.10%). Multinomial logistic regression showed that age, stroke severity, sleep duration, and social support were associated with membership in the low motor–low vitality group, whereas education level was associated with membership in the low cognitive–moderate vitality, psychological, and sensory group. Significant differences were observed in most stroke-specific quality-of-life domains across the identified groups ( p < 0.05). Three distinct intrinsic-capacity profiles were identified among hospitalized older people with stroke, and these profiles were associated with different levels of stroke-specific quality of life. Both clinical and psychosocial factors were associated with profile membership, highlighting the need for targeted interventions to preserve intrinsic capacity and improve quality of life.

Keywords

1 Introduction

Stroke remains the second leading cause of death and long-term disability worldwide and in China and has been identified as a priority chronic disease for prevention and control (1, 2). The burden of stroke increases substantially with age, making older adults a particularly vulnerable population (3). Beyond mortality and disability, stroke has a profound and lasting impact on quality of life (QOL), including physical function, mood, cognition, and social participation, even beyond the acute stage (4, 5).

In 2015, the World Health Organization (WHO) introduced the concept of intrinsic capacity (IC) in the World Report on Aging and Health (6). IC refers to the composite of all physical and mental capacities that an individual can draw on at a given time and includes five core domains: locomotion, vitality, sensory function, cognition, and psychological wellbeing (7). Decline in IC has been associated with poor quality of life, disability, frailty, dependence, and other adverse health outcomes in older adults (8–10). However, much of the existing literature has focused on single functional domains rather than multidimensional IC patterns (6, 10).

Recently, person-centered analytical approaches have been increasingly used to identify multidomain IC profiles in older populations. For example, latent class analysis (LCA) has been used to classify heterogeneous patterns of IC impairment and to explore factors associated with class membership (11, 12). Nevertheless, evidence remains limited regarding IC profiles specifically among hospitalized older people with stroke. This issue is clinically important because people with stroke often present with marked heterogeneity in motor, cognitive, sensory, nutritional, and psychological status, as well as in stroke severity, comorbidity burden, sleep, and social support (13, 14). In addition, major stroke rehabilitation guidelines emphasize multidisciplinary, patient-centered care and individualized goal setting across physical, cognitive, emotional, and participation domains (15).

LCA is a probability-based statistical approach that can identify unobserved subgroups with similar response patterns and therefore provides a useful framework for characterizing multidimensional IC heterogeneity (16). To our knowledge, few studies have applied LCA to examine IC profiles specifically in hospitalized older people with stroke and further linked these profiles to stroke-specific quality of life. Therefore, the aims of this study were to identify latent classes of IC in hospitalized older people with stroke, examine factors associated with class membership, and explore differences in stroke-specific quality of life across these classes. The findings may provide evidence for more individualized rehabilitation and nursing strategies based on domain-specific risk patterns.

2 Methods

This cross-sectional study was reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement (17).

2.1 Study participants

Participants were consecutively recruited from May 2024 to November 2024 from the Department of Neurology, Jiangsu Province Hospital (the First Affiliated Hospital of Nanjing Medical University). Eligible participants met all of the following inclusion criteria: (1) diagnosis of ischemic or hemorrhagic stroke according to the Key Points for Diagnosis of Major Cerebrovascular Diseases in China (2019), confirmed by brain computed tomography (CT) or magnetic resonance imaging (MRI) (18); (2) age ≥60 years; and (3) clinically stable stroke beyond the acute stage, as judged by the treating neurologist on the basis of the medical record and current clinical condition, including patients in the recovery or sequelae stage. To ensure clinical stability, enrollment was performed at least 48 h after admission, when the patient had stable vital signs and no acute deterioration.

Exclusion criteria were as follows: (1) severe cognitive impairment or a history of mental illness; and (2) comorbid cancer or vital organ failure. Severe cognitive impairment was screened at enrollment by trained investigators using the Chinese version of the Mini-Mental State Examination (MMSE), together with an assessment of communication ability and response capacity during the enrollment interview. Participants were excluded if they were unable to understand the study information or interview questions, unable to communicate meaningfully with the investigator, or unable to provide coherent and consistent responses sufficient to complete the questionnaire assessment.

Sample size estimation followed the variable multiple method, yielding a theoretical sample size range of 160–320 (16 variables × 10–20) (18). After allowing for a potential 10% rate of questionnaire loss or invalidity, the minimum required sample size was set at 178.

2.2 Investigation tools

The main study outcomes were intrinsic capacity (five-domain status and total score) and stroke-specific quality of life. Additional measured variables included neurological deficit severity, comorbidity burden, and perceived social support. The instruments were selected to align with the WHO intrinsic capacity framework and the study objectives, and ensure feasibility for bedside assessment in hospitalized older people with stroke. All measures are widely used in stroke/geriatric research and were feasible for bedside assessment in hospitalized older people with stroke.

2.2.1 General information questionnaire

A self-designed questionnaire was administered to collect demographic data (gender, age, body mass index [BMI], education level, marital status), socioeconomic status (employment situation, medical insurance type, residence), lifestyle factors (smoking, alcohol consumption, sleep duration), and stroke-related characteristics (stroke type, stroke severity, limb muscle strength). Limb muscle strength was assessed using the Lovett grading system (0–5). “Normal” was defined as grade 5 in all limbs; “abnormal” was defined as grade ≤ 4 in any limb on either side. Spasticity was not assessed (e.g., Modified Ashworth Scale was not collected).

2.2.2 National Institutes of Health Stroke Scale

Developed in 1989, the National Institutes of Health Stroke Scale (NIHSS) assesses neurological function using 11 items, including level of consciousness, visual fields, limb motor function, sensation, and language (19). Scores range from 0 to 42, with 0 to 1 defined as normal, 2–4 as mild, 5–15 as moderate, and >15 as severe. The NIHSS is a well-validated instrument for quantifying neurological deficit severity in people with stroke (20). The Cronbach's α coefficient for this scale is 0.810.

2.2.3 Age-adjusted Charlson Comorbidity Index

The Age-adjusted Charlson Comorbidity Index (ACCI) is a structured scoring instrument designed to quantify the complexity of patients' clinical conditions and to prospectively estimate mortality risk in those with chronic diseases and cancer (21). This index integrates 19 clinically relevant diseases, each assigned a specific weight according to its potential impact on mortality. The scoring system is based on two primary dimensions. The first dimension, age-stratified scoring, assigns points as follows: 40–49 years, 1 point; 50–59 years, 2 points; 60–69 years, 3 points; and 70 years or older, 4 points. The second dimension, disease burden, accounts for the weighted scores of the 19 specified comorbid conditions. Data on pre-existing comorbidities prior to stroke diagnosis were obtained from medical record review. The cumulative score reflects the overall comorbidity burden, with scores ≥5 indicating multiple comorbidities (high burden) and scores < 5 indicating non-multiple comorbidities. The CCI/ACCI has been extensively evaluated, and evidence supporting its clinimetric properties has been summarized in a critical review (21). The Cronbach's α coefficient for the ACCI is 0.832.

2.2.4 Perceived Social Support Scale

The Perceived Social Support Scale (PSSS) measures multidimensional self-perceived social support (22). It consists of 12 items across three domains: family support (items 3, 4, 8, 11), friend support (items 6, 7, 9, 12), and other support (items 1, 2, 5, 10). Items are rated on a 7-point scale (1–7), with total scores ranging from 12 to 84. Scores of 12–36 indicate low social support, 37–60 indicate moderate social support, and 61–84 indicate high social support. The PSSS has been validated and has shown acceptable psychometric properties in Chinese hospitalized populations (22). The Cronbach's α coefficient for this scale is 0.840.

2.2.5 Intrinsic capacity assessment tools

Intrinsic capacity was assessed according to the World Health Organization's five-domain framework (7). For latent class analysis, each domain was dichotomized as preserved (1) or impaired (0) based on predefined criteria.

Vitality: assessed using the Mini-Nutritional Assessment Short-Form (MNA-SF) (23). Scores < 11 were classified as impaired vitality (risk of malnutrition or malnutrition), whereas scores ≥11 were classified as preserved vitality. The MNA-SF is a widely used and validated screening tool for nutritional status in older people (23). The Cronbach's α coefficient in this study was 0.816.

Psychological: assessed using the 15-item Geriatric Depression Scale (GDS-15) (24). Scores ≥8 indicated impaired psychological status, whereas scores < 8 indicated preserved psychological status. The Chinese version of the GDS-15 has demonstrated acceptable psychometric properties in older Chinese populations (24). The Cronbach's α coefficient in this study was 0.793.

Cognition: assessed using the Mini-Mental State Examination (MMSE). Education-specific cutoffs were used to define cognitive impairment: ≤ 17 for illiterate participants, ≤ 20 for those with primary school education, and ≤ 24 for those with junior high school education or above (25). Scores above these thresholds were classified as preserved cognition. The Chinese version of the MMSE was used, and measurement invariance has been examined in older Chinese populations (25). The Cronbach's α coefficient in this study was 0.838.

Locomotion (motor): assessed using the Short Physical Performance Battery (SPPB), which includes standing balance, 4-m walking speed, and the five-times sit-to-stand test (26, 27). Scores ≤ 8 indicated impaired locomotion, whereas scores >8 indicated preserved locomotion. The SPPB is a validated measure of lower-extremity physical performance and has been widely used in older-adult populations (27). The Cronbach's α coefficient in this study was 0.812.

Sensory: assessed using visual acuity and whisper tests (28). Sensory function was classified as impaired if the participant had abnormal visual acuity and/or an abnormal whisper test result; otherwise, it was classified as preserved. Normal visual function was defined as uncorrected bilateral visual acuity ≥0.8.

The total intrinsic capacity score ranged from 0 to 5, with higher scores indicating better overall intrinsic capacity.

2.2.6 Stroke-specific quality of life scale

The Stroke-Specific Quality of Life Scale (SS-QOL) was employed to assess quality of life in people with stroke (29). It comprises 49 items across 12 domains: vision, language, mood, cognition, upper extremity function, personality, mobility, family roles, self-care, energy, work productivity, and social roles. Each item is rated on a 5-point scale, with higher total scores indicating better quality of life. This instrument has demonstrated acceptable validity and reliability in previous studies (30, 31). The Cronbach's α coefficient is 0.832.

2.3 Data collection and quality control

This study was approved by the Ethics Review Committee of the First Affiliated Hospital of Nanjing Medical University (approval number: 2024-SR-100). All participants provided written informed consent before the interview-based assessment. Data collection was conducted by two graduate nursing students who had received standardized training before the study, including training in questionnaire administration, communication procedures, and assessment consistency.

After informed consent was obtained, trained investigators conducted structured, interviewer-administered, one-to-one assessments using standardized paper questionnaires. Before formal data collection, participants' ability to understand the interview content and provide reliable responses was verified during enrollment screening. Demographic and clinical information was primarily obtained from the hospital electronic medical record system, and missing items were supplemented through direct inquiry of participants or family members whenever possible.

Eligibility screening was completed before questionnaire administration. A total of 322 eligible participants were enrolled and underwent the interviewer-administered assessment. Eight questionnaires were subsequently excluded in their entirety because of invalid or incomplete responses, resulting in 314 valid questionnaires and an effective response rate of 97.5%.

2.4 Statistical analysis

Data entry followed a double-independent process with cross-verification. Latent class models were constructed using Mplus version 8.0 (Muthén & Muthén, Los Angeles, CA, United States) Model fit was evaluated using the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and sample-size adjusted BIC (aBIC), with lower values indicating better fit. The optimal number of classes was determined using the Lo-Mendell-Rubin adjusted likelihood ratio test (LMR-LRT) and bootstrap likelihood ratio test (BLRT), with p < 0.05 indicating that the k-class model fit significantly better than the (k−1)-class model. Classification accuracy was assessed using entropy, with values >0.80 indicating good classification quality (32).

Statistical analyses were performed using SPSS version 27.0 (IBM Corp., Armonk, NY, United States). Normality of continuous variables was assessed using the Shapiro–Wilk test together with visual inspection of histograms and Q–Q plots. Homogeneity of variances was examined using Levene's test before ANOVA. Normally distributed data are presented as mean ± standard deviation and were compared using one-way analysis of variance (ANOVA), followed by least significant difference (LSD) post-hoc tests where appropriate. Skewed data are presented as median (interquartile range) and were compared using non-parametric tests. Categorical variables are described as frequencies and percentages and were compared using the chi-square test.

To examine factors associated with latent-class membership, multinomial logistic regression was performed with the Function Maintenance Group as the reference category. Candidate predictors were selected based on both their univariate association with latent-class membership and their theoretical or clinical relevance to intrinsic capacity in older people with stroke, as informed by previous literature and the study objectives. Variables entered into the model included age group, education level, sleep duration, stroke severity, and perceived social support. For regression analysis, education level was recategorized into three groups (primary school or below, junior high school, and senior high school or above), and sleep duration was dichotomized as >6 h vs. ≤ 6 h. Categorical predictors were dummy coded, and reference categories were specified in Table 1. Multicollinearity among independent variables was assessed using variance inflation factors (VIFs), and no serious multicollinearity was detected. Odds ratios (ORs), 95% confidence intervals (CIs), and p-values are reported for all model estimates. A two-sided p-value < 0.05 was considered statistically significant.

Table 1. Multivariate logistic regression analysis of factors associated with latent classes of intrinsic capacity.

PredictorReference categoryLow motor–low vitality group vs. function maintenance group OR (95% CI)pLow cognitive–moderate vitality, psychological and sensory group vs. function maintenance group OR (95% CI)p
Age 60–74 years≥75 years0.365 (0.185–0.718)0.0040.452 (0.204–1.002)0.051
Mild stroke severityModerate/severe stroke0.158 (0.064–0.393)< 0.0010.965 (0.233–3.987)0.960
Low social supportHigh social support3.420 (1.158–10.103)0.0260.560 (0.112–2.808)0.481
Moderate social supportHigh social support1.994 (1.070–3.717)0.0300.639 (0.303–1.346)0.239
Primary school or belowSenior high school or above0.643 (0.285–1.453)0.2890.191 (0.043–0.859)0.031
Junior high schoolSenior high school or above1.595 (0.833–3.054)0.1591.712 (0.870–3.370)0.120
Sleep duration >6 h≤ 6 h0.424 (0.231–0.779)0.0060.526 (0.271–1.021)0.058

Multinomial logistic regression was performed with latent class membership as the dependent variable. The Function Maintenance Group was used as the reference outcome category. Reference categories for predictors were as follows: age ≥75 years, moderate/severe stroke, high social support, senior high school or above, and sleep duration ≤ 6 h. OR, odds ratio; CI, confidence interval.

Homogeneity of variances across latent classes was examined using Levene's test before one-way ANOVA. For SS-QOL domains with homogeneous variances, one-way ANOVA followed by least significant difference (LSD) post-hoc comparisons was used. For domains with unequal variances, pairwise comparisons were interpreted using the Games–Howell procedure. Pairwise comparison results are reported as mean differences, 95% confidence intervals (CIs), and p-values. No additional adjustment for multiple testing across SS-QOL domains was applied.

After the optimal latent class model was selected in Mplus, each participant was assigned to the class with the highest posterior probability (most likely class membership). This assigned class variable was then exported to SPSS and used in subsequent analyses, including chi-square tests, one-way ANOVA, and multinomial logistic regression. Classification uncertainty was not directly incorporated into these downstream analyses.

3 Results

3.1 General information

A total of 314 older people with stroke were included in this study, consisting of 207 males and 107 females. Participant age ranged from 60 to 86 years, with a mean ± standard deviation of 68.97 ± 6.36 years. Additional demographic and clinical information are presented in Table 2.

Table 2. Demographic and clinical characteristics of older adults with stroke.

VariableCategoryNumberPercentage (%)
GenderMale20765.9
Female10734.1
Age60–74years24979.3
≥75years6520.7
BMI< 18.5113.5
18.5–24.918358.3
≥2512038.2
Educational levelIlliterate185.7
Primary school3611.5
Junior high school9329.6
Senior high school/technical secondary school9530.2
College or above7222.9
Marital statusMarried29694.2
Single92.9
Divorced/widowed92.9
Employment situationEmployed5316.9
Unemployed/retired26183.1
Medical insurance typeEmployee medical insurance12038.2
Resident medical insurance103.2
Non-local medical insurance16853.5
Self-pay165.1
ResidenceUrban21969.7
Rural9530.3
SmokingYes8025.5
Never/quit23474.5
Alcohol consumptionYes8226.1
Never/quit23273.9
Sleep duration>6 h16051.0
4–6 h6019.1
< 4 h9429.9
Stroke typeHemorrhagic stroke6019.1
Ischemic stroke25480.9
Limb muscle strength (Lovett grade)aNormal23574.8
Abnormal7925.2
Stroke severity (NIHSS)Mild stroke28189.5
Moderate/severe stroke3310.5
ACCIMultiple comorbidities10934.7
Non-multiple comorbidities20565.3
PSSSLow social support227.0
Moderate social support11837.6
High social support17455.4

aLimb muscle strength was assessed using the Lovett grading system (0–5). Normal = grade 5 in all limbs; abnormal = grade ≤ 4 in any limb on either side.

BMI, body mass index; NIHSS, National Institutes of Health Stroke Scale; ACCI, Age-adjusted Charlson Comorbidity Index; PSSS, Perceived Social Support Scale.

3.2 LCA of intrinsic capacity in older people with stroke

The median intrinsic capacity score of older people with stroke was 4 (interquartile range: 3–5). A decline in at least one dimension of intrinsic capacity was observed in 72.0% (226 cases) of people with stroke. Among the five dimensions, decline in the vitality dimension was most common (44.9%, 141 cases), followed by motor (32.5%, 102 cases), sensory (29.6%, 93 cases), cognitive (29.0%, 91 cases), and psychological (12.4%, 39 cases) dimensions. LCA was performed using the five dimensions as manifest variables. Although AIC, BIC, and sample-size aBIC values decreased as the number of classes increased, and the BIC reached its lowest value in the two-class model, the three-class model achieved the optimal entropy value, indicating superior classification accuracy. Furthermore, the Lo-Mendell-Rubin (LMR) and Bootstrap Likelihood Ratio Test (BLRT) results were statistically significant (p < 0.05), confirming that the three-class model was significantly better than the two-class model (Table 3). Comprehensive evaluation of multiple fit indices confirmed the three-class model as optimal. The average posterior probabilities for the three classes were 0.879, 0.976, and 1.000, respectively, demonstrating high stability and reliability of classification. Based on the highest posterior probability, participants were assigned to one of the three latent classes for subsequent analyses. Figure 1 presents the conditional probability distribution from the Mplus software analysis, illustrating the characteristics of the three latent classes of intrinsic capacity. Class 1 (C1): Low Motor–Low Vitality Group–87 participants (27.7%) with markedly reduced motor and vitality dimensions; Class 2 (C2): Low Cognitive–Moderate Vitality, Psychological, and Sensory Group–54 participants (17.2%) with significant decline in cognitive function and moderate impairment in vitality, psychological, and sensory dimensions; Class 3 (C3): Function Maintenance Group–173 participants (55.1%) with relatively preserved functional dimensions.

Table 3. Fit indices for latent class analysis models of intrinsic capacity.

ModelKAICBICaBICLMR (P)BLRT (P)EntropyClass probability (%)
Model 151833.2091851.9561836.098–––100
Model 2111774.9431816.1871781.298< 0.001< 0.0010.51451.59/48.41
Model 3171770.4631834.2031780.284< 0.001< 0.0010.90727.70/17.20/55.10
Model 4231770.1241856.361783.4110.20930.13040.8941.91/21.98/17.20/58.91
Model 5291774.9191883.6511791.6710.59950.26670.79614.33/19.74/6.69/2.23/57.01
Conditional probability profiles of three latent classes of intrinsic capacity.
Figure 1. Conditional probability profiles of three latent classes of intrinsic capacity.

3.3 Comparison of characteristics among older people with stroke in different latent classes

Comparison of demographic and clinical characteristics among the three latent classes demonstrated no statistically significant differences in gender, BMI, marital status, employment status, residence, smoking, alcohol consumption, or stroke type (all p > 0.05). Variables with significant differences are presented in Table 4.

Table 4. Comparison of demographic and clinical variables across latent classes of intrinsic capacity.

VariableCategoryLow motor-low vitality group (n = 87)Low cognitive-moderate vitality, psychological and sensory group (n = 54)Function maintenance group (n = 173)StatisticP
Age [n (%)]60–74 years58 (66.7)41 (75.9)150 (86.7)X2 = 14.612< 0.001
≥75 years29 (33.3)13 (24.1)23 (13.3)
Educational level [n (%)]Illiterate6 (6.9)0 (0.0)12 (6.9)X2 = 17.8620.022
Primary school7 (8.0)2 (3.7)27 (15.6)
Junior high school28 (32.2)22 (40.7)43 (24.9)
High school/Technical secondary school28 (32.2)21 (38.9)46 (26.6)
College and above18 (20.7)9 (16.7)45 (26.0)
Medical insurance type [n (%)]Employee medical insurance26 (29.9)23 (42.6)71 (41.0)X2 = 14.3510.026
Resident medical insurance4 (4.6)0 (0.0)6 (3.5)
Non-local medical insurance47 (54.0)30 (55.6)91 (52.6)
Self-pay10 (11.5)1 (1.9)5 (2.9)
Limb muscle strength (Lovett grade)a [n (%)]Normal47 (54.0)42 (77.8)146 (84.4)X2 = 28.665< 0.001
Abnormal40 (46.0)12 (22.2)27 (15.6)
Sleep duration [n (%)]>6 h33 (37.9)22 (40.7)105 (60.7)X2 = 19.397< 0.001
4–6 h17 (19.5)10 (18.5)33 (19.1)
< 4 h37 (42.5)22 (40.7)35 (20.2)
Stroke severity (NIHSS) [n (%)]Mild65 (74.7)51 (94.4)165 (95.4)X2 = 27.982< 0.001
Moderate/severe22 (25.3)3 (5.6)8 (4.6)
ACCI [n (%)]Multiple comorbidities40 (46.0)19 (35.2)50 (28.9)X2 = 7.4540.024
Non-multiple comorbidities47 (54.0)35 (64.8)123 (71.1)
PSSS [n (%)]Low support level8 (9.2)2 (3.7)12 (6.9)X2 = 12.1760.016
Medium support level42 (48.3)13 (24.1)63 (36.4)
High support level37 (42.5)39 (72.2)98 (56.6)

aNormal = Lovett grade 5 in all limbs; abnormal = grade ≤ 4 in any limb on either side.

NIHSS, National Institutes of Health Stroke Scale (higher scores indicate more severe stroke); ACCI, Age-adjusted Charlson Comorbidity Index (higher scores indicate greater comorbidity burden); PSSS, Perceived Social Support Scale (higher scores indicate higher perceived social support).

Data are presented as n (%). Group comparisons were conducted using the chi-square test. Significant p-values are shown in bold (P < 0.05).

3.4 Association between latent classes of intrinsic capacity and quality of life scores

One-way ANOVA was conducted to compare SS-QOL scores across the three latent classes. Significant between-class differences were observed for all domains except personality (F = 1.487, p = 0.228). Levene's test indicated unequal variances for vision, language, mood, mobility, self-care, upper extremity function, and work productivity; therefore, Games–Howell post-hoc tests were used for these domains. LSD post-hoc tests were used for the remaining domains with homogeneous variances. Compared with the Function Maintenance Group, the Low Motor–Low Vitality Group had significantly lower scores in vision, language, mood, cognition, upper extremity function, mobility, family roles, self-care, energy, work productivity, and social roles, whereas personality did not differ significantly. Compared with the Function Maintenance Group, the Low Cognitive–Moderate Vitality, Psychological, and Sensory Group had significantly lower scores in cognition, family roles, and social roles, whereas the remaining domains did not differ significantly. Detailed results, including Levene's test results, mean differences, 95% confidence intervals, and p-values for pairwise comparisons, are presented in Table 5.

Table 5. A quality of life scores across latent classes of intrinsic capacity.

DimensionScore rangeLow motor–low vitality group (n = 87)Low cognitive–moderate vitality, psychological, and sensory group (n = 54)Function maintenance group (n = 173)Levene's pFPPost-hoc methodLow motor vs. function maintenance MD (95% CI), pLow cognitive vs. function maintenance md (95% CI), pLow motor vs. low cognitive MD (95% CI), p
Vision3–1513.00 ± 2.04014.06 ± 1.08914.35 ± 1.009< 0.00127.692< 0.001Games–Howell−1.347 (−1.90, −0.80), < 0.001−0.291 (−0.69, 0.11), 0.195−1.056 (−1.68, −0.43), < 0.001
Language5–2522.30 ± 3.43823.61 ± 2.79124.21 ± 1.575< 0.00117.690< 0.001Games–Howell−1.909 (−2.83, −0.99), < 0.001−0.597 (−1.55, 0.36), 0.298−1.312 (−2.57, −0.06), 0.038
Mood5–2520.56 ± 4.03721.28 ± 5.16322.34 ± 3.3190.0056.3660.002Games–Howell−1.778 (−2.96, −0.59), 0.002−1.063 (−2.85, 0.73), 0.334−0.715 (−2.68, 1.25), 0.663
Cognition3–159.44 ± 3.1909.15 ± 3.02411.65 ± 3.1280.74121.634< 0.001LSD−2.216 (−3.03, −1.41), < 0.001−2.505 (−3.46, −1.55), < 0.0010.289 (−0.78, 1.35), 0.595
Upper extremity function5–2520.82 ± 4.28523.83 ± 2.75924.39 ± 1.437< 0.00150.473< 0.001Games–Howell−3.577 (−4.70, −2.45), < 0.001−0.560 (−1.50, 0.38), 0.331−3.017 (−4.42, −1.61), < 0.001
Personality3–1511.37 ± 3.27512.06 ± 3.25312.06 ± 3.0910.6491.4870.228LSD−0.690 (−1.51, 0.13), 0.0990.002 (−0.97, 0.97), 0.996−0.688 (−1.77, 0.39), 0.211
Mobility6–3021.86 ± 4.66628.72 ± 1.67628.47 ± 2.911< 0.001125.084< 0.001Games–Howell−6.606 (−7.90, −5.31), < 0.0010.254 (−0.50, 1.01), 0.704−6.860 (−8.17, −5.56), < 0.001
Family roles3–1510.15 ± 2.75110.80 ± 3.12912.11 ± 2.7230.20715.381< 0.001LSD−1.960 (−2.69, −1.24), < 0.001−1.314 (−2.17, −0.45), 0.003−0.647 (−1.60, 0.31), 0.184
Self-care5–2521.08 ± 4.66924.20 ± 2.18424.58 ± 1.281< 0.00147.669< 0.001Games–Howell−3.503 (−4.72, −2.29), < 0.001−0.380 (−1.13, 0.37), 0.448−3.123 (−4.50, −1.74), < 0.001
Energy3–158.85 ± 3.38410.11 ± 3.57010.39 ± 3.6240.9885.5240.004LSD−1.537 (−2.45, −0.62), 0.001−0.276 (−1.37, 0.81), 0.618−1.261 (−2.47, −0.05), 0.041
Work productivity3–159.95 ± 2.90511.52 ± 2.62612.40 ± 2.3650.01126.347< 0.001Games–Howell−2.451 (−3.30, −1.60), < 0.001−0.886 (−1.84, 0.07), 0.075−1.564 (−2.69, −0.44), 0.004
Social roles5–2512.63 ± 5.38315.56 ± 5.96417.33 ± 5.7930.71919.597< 0.001LSD−4.697 (−6.17, −3.22), < 0.001−1.774 (−3.53, −0.02), 0.047−2.923 (−4.87, −0.98), 0.003

SS-QOL, Stroke-Specific Quality of Life; MD, mean difference; CI, confidence interval.

Values are presented as mean ± standard deviation. Homogeneity of variances was assessed using Levene's test. LSD post-hoc comparisons were used for domains with homogeneous variances, whereas Games–Howell post-hoc comparisons were used for domains with unequal variances. No additional adjustment for multiple testing across domains was applied.

3.5 Multivariate logistic regression analysis of factors influencing latent classes

The three latent classes identified by LCA were treated as the dependent variable in the multinomial logistic regression model, with the Function Maintenance Group used as the reference category. Predictor coding and reference categories are shown in Table 1. Full model estimates, including non-significant associations, are presented in Table 1.

Compared with the Function Maintenance Group, participants aged 60–74 years (OR = 0.365, 95% CI: 0.185–0.718, p = 0.004), those with mild stroke severity (OR = 0.158, 95% CI: 0.064–0.393, p < 0.001), and those with sleep duration > 6 h (OR = 0.424, 95% CI: 0.231–0.779, p = 0.006) had lower odds of belonging to the Low Motor–Low Vitality Group. In contrast, participants with low social support (OR = 3.420, 95% CI: 1.158–10.103, p = 0.026) and moderate social support (OR = 1.994, 95% CI: 1.070–3.717, p = 0.030), compared with those with high social support, had higher odds of belonging to this group. Education level was not significantly associated with membership in the Low Motor–Low Vitality Group after adjustment.

For the Low Cognitive–Moderate Vitality, Psychological and Sensory Group, participants with primary school education or below had lower odds of belonging to this group than those with senior high school education or above (OR = 0.191, 95% CI: 0.043–0.859, p = 0.031). Age and sleep duration showed borderline associations with membership in this group, whereas stroke severity, social support, and junior high school education were not significantly associated with class membership after adjustment.

4 Discussion

4.1 Group heterogeneity in intrinsic capacity

This study identified three distinct intrinsic-capacity (IC) profiles among hospitalized older people with stroke, and most participants showed decline in at least one IC domain. These findings highlight substantial heterogeneity in multidomain IC impairment in this population.

Overall, 72.0% of participants experienced decline in at least one intrinsic-capacity domain, which is slightly higher than the 69.1% reported in general older populations (8). Older people with stroke often experience concurrent impairments in motor, cognitive, sensory, nutritional, and psychological function, which may contribute to reduced quality of life and functional decline (33). The three-class solution therefore provides a useful framework for characterizing these multidimensional patterns.

The Function Maintenance Group (55.1%) showed relatively preserved function across IC domains, although some decline remained, particularly in vitality. This finding suggests that even older people with stroke who appear functionally stable may still have early nutritional vulnerability requiring attention (34).

The Low Motor–Low Vitality Group (27.7%) was characterized by marked impairment in locomotion and vitality. These deficits may be mutually reinforcing, as mobility limitation can reduce daily activity and nutritional intake, while malnutrition may aggravate sarcopenia, weakness, and further mobility decline (34, 35).

The Low Cognitive–Moderate Vitality, Psychological, and Sensory Group (17.2%) showed prominent cognitive impairment together with moderate deficits in vitality, psychological status, and sensory function. This pattern is consistent with the high prevalence of post-stroke cognitive impairment and its frequent co-occurrence with nutritional problems, depressive symptoms, and sensory dysfunction (33, 36–40).

Taken together, these findings suggest that IC profiling may provide added value beyond isolated single-domain assessment by identifying co-occurring patterns of decline. In clinical practice, such profiling may help clinicians prioritize rehabilitation goals, tailor multidisciplinary care, and identify patients who require closer follow-up after hospitalization (50). For example, the Low Motor–Low Vitality Group may benefit most from integrated mobility and nutritional support, whereas the Low Cognitive–Moderate Vitality, Psychological, and Sensory Group may require greater emphasis on cognitive rehabilitation, psychological support, and sensory/environmental management. For the Function Maintenance Group, IC profiling may help support early prevention and continued monitoring before further decline emerges.

4.2 Influencing factors of intrinsic capacity latent classes

4.2.1 Demographic characteristics

The multinomial logistic regression results showed that age, stroke severity, sleep duration, and social support were mainly associated with membership in the Low Motor–Low Vitality Group, whereas education level was associated with membership in the Low Cognitive–Moderate Vitality, Psychological, and Sensory Group. These findings suggest that different intrinsic-capacity profiles may have distinct correlates in hospitalized older people with stroke, which is broadly consistent with previous research (41).

Participants aged 60–74 years were less likely to be classified into the Low Motor–Low Vitality Group than those aged 75 years or older, suggesting that relatively younger stroke survivors tend to preserve better intrinsic capacity. Age-related decline in intrinsic capacity may reflect the combined effects of physiological aging, reduced physiological reserve, and accumulated disease burden (9, 49).

Education level was associated with membership in the Low Cognitive–Moderate Vitality, Psychological, and Sensory Group. In our data, participants with primary school education or below had lower odds of belonging to this group than those with senior high school education or above. This finding should be interpreted cautiously. Educational attainment may reflect not only health literacy and self-management capacity, but also cognitive reserve, which can modify how cognitive impairment manifests after stroke. Recent evidence from a pooled cohort study suggested that post-stroke cognitive trajectories vary by both education level and cognitive domain; compared with stroke survivors with lower educational attainment, those with higher education may have greater cognitive reserve, yet may also show steeper decline in specific domains such as executive function (42). Given the heterogeneity of post-stroke cognitive trajectories across educational strata and cognitive domains, future longitudinal studies using domain-specific cognitive measures are needed to clarify how education shapes intrinsic-capacity profiles after stroke.

Longer sleep duration (>6 h) was associated with lower odds of belonging to the Low Motor–Low Vitality Group and showed a borderline association with the Low Cognitive–Moderate Vitality, Psychological, and Sensory Group. Adequate sleep may support neuroplasticity, reduce fatigue, and promote recovery after stroke, whereas insufficient sleep may adversely affect physical, cognitive, and psychological function (43). These findings underscore the importance of identifying and managing sleep problems in older people with stroke during hospitalization and rehabilitation.

4.2.2 Stroke severity and social support

Participants with mild stroke had substantially lower odds of belonging to the Low Motor–Low Vitality Group than those with moderate or severe stroke, indicating that worse neurological deficit is linked to poorer intrinsic-capacity status. This finding is consistent with prior studies (44). Neurological deficits affect core functions such as motor ability, cognition, swallowing, and sensory integration, which may in turn compromise daily functioning and quality of life and accelerate intrinsic-capacity decline (45). Routine evaluation of stroke severity and rehabilitation progress is therefore important for individualized care planning and targeted rehabilitation.

Lower levels of perceived social support were associated with higher odds of belonging to the Low Motor–Low Vitality Group. Compared with participants reporting high social support, those with low or moderate social support were more likely to fall into this more impaired profile. Social support may enhance coping ability, treatment adherence, emotional resilience, and engagement in rehabilitation, thereby helping preserve intrinsic capacity (46). These findings support the importance of strengthening family involvement, peer support, and communication between healthcare professionals and patients during stroke recovery (15, 46).

Notably, the factors associated with latent-class membership in this study included both relatively non-modifiable characteristics (such as age and educational attainment) and potentially modifiable factors (such as sleep duration and social support). This distinction is clinically meaningful because non-modifiable factors may help identify vulnerable subgroups, whereas modifiable factors may represent practical intervention targets during hospitalization and early recovery.

4.2.3 Quality of life

Significant differences in stroke-specific quality of life were observed among the latent classes in 11 of the 12 SS-QOL domains, with no significant difference in personality. Overall, the Function Maintenance Group showed the most favorable quality-of-life profile, whereas the Low Motor–Low Vitality Group had the lowest scores across multiple domains. These findings further support the close relationship between intrinsic capacity status and quality of life in older people with stroke (47).

Pairwise comparisons showed that, compared with the Function Maintenance Group, the Low Motor–Low Vitality Group had significantly lower scores in energy, family roles, language, mobility, mood, self-care, social roles, cognition, upper extremity function, vision, and work productivity. This pattern suggests that combined decline in locomotion and vitality may have broad adverse effects on daily functioning, participation, and overall wellbeing, which is consistent with previous findings (48).

In contrast, compared with the Function Maintenance Group, the Low Cognitive–Moderate Vitality, Psychological, and Sensory Group showed significant disadvantages in cognition, family roles, and social roles. This pattern suggests that this profile may be associated not only with difficulties in cognitive functioning and family role performance, but also with reduced social participation in daily life. These findings are broadly consistent with previous evidence indicating that post-stroke multidomain deficits may substantially affect everyday functioning and quality of life (8).

4.3 Implications

Intrinsic-capacity (IC) profiling may have important practical and policy implications for the care of people with stroke. In clinical practice, IC profiling may help clinicians rapidly identify heterogeneous care needs and prioritize domain-focused rehabilitation and nursing strategies across subgroups of people with stroke. It may also support more structured multidisciplinary communication by helping physicians, nurses, therapists, dietitians, and family caregivers focus on the domains most likely to limit recovery and quality of life. At the policy level, incorporating routine IC screening and profiling into inpatient stroke care and discharge planning may support risk stratification and facilitate targeted referral pathways, including rehabilitation, nutrition, sleep management, and social-support services. In the longer term, such an approach may contribute to more efficient allocation of rehabilitation resources and more individualized continuity-of-care models for older people with stroke.

4.4 Limitations

This study has several limitations. First, the cross-sectional design precludes any inference of causality regarding the associations among intrinsic-capacity profiles, influencing factors, and stroke-specific quality of life. Longitudinal studies are needed to examine how intrinsic-capacity profiles evolve over time and how they relate to subsequent recovery and quality-of-life outcomes.

Second, this was a single-center study conducted in a tertiary hospital, which may limit the generalizability of the findings to other settings, such as community-based populations, primary care institutions, or hospitals in other regions. In addition, although participants were identified as being beyond the acute stage on the basis of medical-record documentation and clinical stability, exact time since stroke onset was not consistently available for all participants. Therefore, stroke stage was determined pragmatically rather than by a standardized onset-to-assessment time threshold, which may have introduced some imprecision in stage classification.

Third, after latent class analysis, each participant was assigned to the class with the highest posterior probability for subsequent analyses. Although the entropy and posterior probabilities indicated good classification quality, classification uncertainty was not directly incorporated into the regression and ANOVA analyses, which may have led to some degree of misclassification bias.

Fourth, several measures relied on questionnaire-based or interviewer-administered assessments and may therefore have been subject to reporting bias or information bias. In particular, some psychosocial and lifestyle variables, such as perceived social support and sleep duration, may be influenced by subjective perception.

Finally, although we adjusted for several demographic, clinical, and psychosocial variables, residual confounding cannot be ruled out. Additional multicenter longitudinal studies with more detailed stroke-related information and domain-specific cognitive assessment are warranted to validate and extend these findings.

5 Conclusion

This study identified three distinct intrinsic-capacity profiles among hospitalized older people with stroke, and these profiles were associated with different levels of stroke-specific quality of life. Age, educational level, sleep duration, stroke severity, and social support were important factors associated with profile membership. These findings suggest that intrinsic-capacity profiling may help identify heterogeneous functional needs in this population and support more individualized rehabilitation and nursing strategies. In clinical practice, targeted, domain-focused interventions may be particularly important for people with stroke who are older, have lower educational attainment, shorter sleep duration, more severe neurological deficits, or lower social support. From a broader practice and policy perspective, integrating routine IC screening and profiling into inpatient stroke care and discharge planning may improve risk stratification, continuity of care, and allocation of supportive rehabilitation resources for high-risk older people with stroke.

Generative AI statement

The author(s) declared that Generative AI was not used in the creation of this manuscript.

Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Footnotes

Edited by: Brijesh Sathian, Hamad Medical Corporation, Qatar

Reviewed by: Elisângela Cristina Ramos Hernandes, Universidade Cidade de São Paulo, Brazil

Xin Luo, Beijing Forestry University, China

Footnotes

LCA, Latent Class Analysis; NIHSS, National Institutes of Health Stroke Scale; ACCI, Age-adjusted Charlson Comorbidity Index; PSSS, Perceived Social Support Scale; MNA-SF, Mini-Nutritional Assessment Short-Form; GDS-15, 15-item Geriatric Depression Scale; MMSE, Mini-Mental State Examination; SPPB, Short Physical Performance Battery; SS-QOL, Stroke-Specific Quality of Life Scale; AIC, Akaike Information Criterion; BIC, Bayesian Information Criterion; aBIC, adjusted Bayesian Information Criterion.

Statements

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding

The author(s) declared that financial support was received for this work and/or its publication. This research was supported by the Nursing Foundation of the First Affiliated Hospital of Nanjing Medical University (No. 15).

Data availability

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics approval

The studies involving humans were approved by the Ethics Committee of the First Affiliated Hospital with Nanjing Medical University (2024-SR-100). The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

Author contributions

X-XT: Formal analysis, Methodology, Project administration, Writing – original draft, Writing – review & editing. Q-jW: Project administration, Supervision, Validation, Writing – review & editing. X-MZ: Supervision, Validation, Visualization, Writing – original draft. Q-YT: Conceptualization, Data curation, Investigation, Writing – original draft. LY: Funding acquisition, Methodology, Resources, Writing – original draft, Writing – review & editing.

Acknowledgements

We would like to acknowledge the hard and dedicated work of all the staff who implemented the intervention and evaluation components of the study.

References

  1. 1. Feigin VL, Brainin M, Norrving B, Martins SO, Pandian J, Lindsay P. World stroke organization: global stroke fact sheet 2025. Int J Stroke. (2025) 20: 132 – 44. doi: doi:10.1177/17474930241308142
  2. 2. Hilkens NA, Casolla B, Leung TW, de Leeuw FE. Stroke. Lancet. (2024) 403: 2820 – 36. doi: doi:10.1016/S0140-6736(24)00642-1
  3. 3. GBD 2019 Stroke Collaborators. Global, regional, and national burden of stroke and its risk factors, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet Neurol. (2021) 20: 795 – 820. doi: doi:10.1016/S1474-4422(21)00252-0
  4. 4. Youkee D, Deen GF, Sackley C, Lisk DR, Marshall I, Soley-Bori M. Quality of life and quality-adjusted life years after stroke in Sierra Leone. Int J Stroke. (2024) 19: 981 – 8. doi: doi:10.1177/17474930241249589
  5. 5. Cheon S, Li CY, Jeng JS, Wang JD, Ku LE. The lifetime burden following stroke: long term impact of stroke on survival and quality of life. Int J Stroke. (2023) 18: 795 – 803. doi: doi:10.1177/17474930231165607
  6. 6. Beard JR, Officer A, de Carvalho IA, Sadana R, Pot AM, Michel JP. The World report on ageing and health: a policy framework for healthy ageing. Lancet. (2016) 387: 2145 – 54. doi: doi:10.1016/S0140-6736(15)00516-4
  7. 7. Integrated Care for Older People: Guidelines on Community-Level Interventions to Manage Declines in Intrinsic Capacity [EB/OL] (2017). Available online at: (Accessed February 18, 2025).
  8. 8. Yang Y, Ma G, Wei S, Wei X, Yan B, Yuan Y. Adverse outcomes of intrinsic capacity in older adults: a scoping review. Arch Gerontol. Geriatr. (2024) 120: 105335. doi: doi:10.1016/j.archger.2024.105335
  9. 9. Meng LC, Chuang HM, Lu WH, Lee WJ, Liang CK, Loh CH. Multi-trajectories of intrinsic capacity decline and their impact on age-related outcomes: a 20-year national longitudinal cohort study. Aging Dis. (2023) 15: 2697 – 709. doi: doi:10.14336/AD.2023.1115-1
  10. 10. Wei X, Chen Y, Qin J, Yang Y, Yang T, Yan F. Factors associated with the intrinsic capacity in older adults: a scoping review. J Clin Nurs. (2024) 33: 1739 – 50. doi: doi:10.1111/jocn.17017
  11. 11. Zhu L, Shen X, Shi X, Ouyang X. Factors associated with intrinsic capacity impairment in hospitalized older adults: a latent class analysis. BMC Geriatr. (2024) 24: 494. doi: doi:10.1186/s12877-024-05093-z
  12. 12. Cheng Y, Li W, Xiao S, Chen Y, Qi X. Intrinsic capacity and its dimensions in relation to functional ability in older adults: a systematic review and meta-analysis. Arch Gerontol Geriatr. (2025) 135: 105860. doi: doi:10.1016/j.archger.2025.105860
  13. 13. Chun M, Qin H, Turnbull I, Sansome S, Gilbert S, Hacker A. Heterogeneity in the diagnosis and prognosis of ischemic stroke subtypes: 9-year follow-up of 22,000 cases in Chinese adults. Int J Stroke. (2023) 18: 847 – 55. doi: doi:10.1177/17474930231162265
  14. 14. Buvarp D, Viktorisson A, Axelsson F, Lehto E, Lindgren L, Lundström E. Physical activity trajectories and functional recovery after acute stroke among adults in Sweden. JAMA Netw Open. (2023) 6: e2310919. doi: doi:10.1001/jamanetworkopen.2023.10919
  15. 15. Winstein CJ, Stein J, Arena R, Bates B, Cherney LR, Cramer SC. Guidelines for Adult Stroke Rehabilitation and Recovery: a Guideline for Healthcare Professionals From the American Heart Association/American Stroke Association. Stroke. (2016) 47: e98 – 169. doi: doi:10.1161/STR.0000000000000098
  16. 16. Mori M, Krumholz HM, Allore HG. Using latent class analysis to identify hidden clinical phenotypes. JAMA. (2020) 324: 700 – 1. doi: doi:10.1001/jama.2020.2278
  17. 17. von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP. The The strengthening the reporting of observational studies in epidemiology (strobe) statement: guidelines for reporting observational studies. PLoS Med. (2007) 4: e296. doi: doi:10.1371/journal.pmed.0040296
  18. 18. Chinese Guidelines for Diagnosis of Various Cerebrovascular Diseases. Chinese Journal of Neurology (2019). Available online at: (Accessed March 16, 2025).
  19. 19. Feigin VL, Norrving B, Mensah GA. Global burden of stroke. Circ Res. (2017) 120: 439 – 48. doi: doi:10.1161/CIRCRESAHA.116.308413
  20. 20. Brott T, Adams HP, Olinger CP, Marler JR, Barsan WG, Biller J. Measurements of acute cerebral infarction: a clinical examination scale. Stroke. (1989) 20: 864 – 70. doi: doi:10.1161/01.STR.20.7.864
  21. 21. Charlson ME, Carrozzino D, Guidi J, Patierno C. Charlson comorbidity index: a critical review of clinimetric properties. Psychother Psychosom. (2022) 91: 8 – 35. doi: doi:10.1159/000521288
  22. 22. Zhang F, Zhu SZ, Deng PJ. Evaluation of the Perceived Social Support Scale's application in social support research for hospitalized patients in China. Chin Nurs Res. (2018) 32: 2048 – 52.
  23. 23. Rubenstein LZ, Harker JO, Salvà A, Guigoz Y, Vellas B. Screening for undernutrition in geriatric practice: developing the short-form mini-nutritional assessment (MNA-SF). J Gerontol Ser A, Biol Sci Med Sci. (2001) 56: M366 – 372. doi: doi:10.1093/gerona/56.6.M366
  24. 24. Zhang C, Zhang H, Zhao M, Chen C, Li Z, Liu D. Psychometric properties and modification of the 15-item geriatric depression scale among Chinese oldest-old and centenarians: a mixed-methods study. BMC Geriatr. (2022) 22: 144. doi: doi:10.1186/s12877-022-02833-x
  25. 25. Jiang L, Zeng H, Wu D. Measurement invariance of the Mini-Mental State Examination among older adults with different sex and educational levels. Chin J Clin Psychol. (2022) 30: 940 – 3.
  26. 26. Li W, Rao Z, Fu Y, Schwebel DC, Li L, Ning P. Value of the short physical performance battery (SPPB) in predicting fall and fall-induced injury among old Chinese adults. BMC Geriatr. (2023) 23: 574. doi: doi:10.1186/s12877-023-04290-6
  27. 27. Guralnik JM, Simonsick EM, Ferrucci L, Glynn RJ, Berkman LF, Blazer DG. short physical performance battery assessing lower extremity function: association with self-reported disability and prediction of mortality and nursing home admission. J Gerontol. (1994) 49: M85 – 94. doi: doi:10.1093/geronj/49.2.M85
  28. 28. Integrated Care for Older People (ICOPE) Implementation Framework: Guidance for Systems and Services. Available online at: (Accessed February 19, 2025).
  29. 29. Sakr F, Dabbous M, Akel M, Salameh P, Hosseini H. Construction and validation of the 17-item stroke-specific quality of life scale (SS-QOL-17): a comprehensive short scale to assess the functional, psychosocial, and therapeutic factors of QOL among stroke survivors. Int J Environ Res Public Health. (2022) 19: 15668. doi: doi:10.3390/ijerph192315668
  30. 30. Cao WH, Li J, Guo CH. Development and evaluation of a quality of life scale for patients with stroke. Chin J Geriatr Heart Brain Vessel Dis. (2003) 5: 252 – 4.
  31. 31. Williams LS, Weinberger M, Harris LE, Clark DO, Biller J. Development of a stroke-specific quality of life scale. Stroke. (1999) 30: 1362 – 9. doi: doi:10.1161/01.STR.30.7.1362
  32. 32. Wang MC, Bi XY. Latent Variable Modeling and Mplus Application: Advanced Level. 1st ed. Chongqing University Press (2018). Available online at: (Accessed February 20, 2025). In Chinese.
  33. 33. Report On Stroke Prevention And Treatment In China Writing Group. Summary of the report on stroke prevention and treatment in China 2021. Chin J Cerebrovasc Dis. (2023) 20: 783 – 93.
  34. 34. Di Vincenzo O, Luisi MLE, Alicante P, Ballarin G, Biffi B, Gheri CF. The assessment of the risk of malnutrition (undernutrition) in stroke patients. Nutrients. (2023) 15: 683. doi: doi:10.3390/nu15030683
  35. 35. Papadopoulou SK, Papadimitriou K, Voulgaridou G, Georgaki E, Tsotidou E, Zantidou O. Exercise and nutrition impact on osteoporosis and sarcopenia—the incidence of osteosarcopenia: a narrative review. Nutrients. (2021) 13: 4499. doi: doi:10.3390/nu13124499
  36. 36. Yuan L, Shen J, Ye S, Zhou N. Assessing care dependence status and associated influencing factors among middle-aged hemiplegic stroke patients during the post-acute rehabilitation phase: a correlational study. J Clin Nurs. (2024) 33: 2249 – 58. doi: doi:10.1111/jocn.17124
  37. 37. Sun M, Wang L, Hu Y, Wang X, Yan S, Guo Y. Cognitive impairment mediates the association between dietary inflammation and depressive symptoms in the elderly. Nutrients. (2022) 14: 5118. doi: doi:10.3390/nu14235118
  38. 38. Xu Y, Chen A, Chen R, Zheng W. Association between depressive symptoms and cognitive function in the older population, and the mediating role of neurofilament light chain: evidence from NHANES 2013–2014. J Affect Disord. (2024) 360: 221 – 8. doi: doi:10.1016/j.jad.2024.05.165
  39. 39. Lin FR, Pike JR, Albert MS, Arnold M, Burgard S, Chisolm T. Hearing intervention versus health education control to reduce cognitive decline in older adults with hearing loss in the USA (ACHIEVE): a multicentre, randomised controlled trial. Lancet. (2023) 402: 786 – 97. doi: doi:10.1016/S0140-6736(23)01406-X
  40. 40. Kwan RYC, Kwan CW, Kor PPK, Chi I. Cognitive decline, sensory impairment, and the use of audio-visual aids by long-term care facility residents. BMC Geriatr. (2022) 22: 216. doi: doi:10.1186/s12877-022-02895-x
  41. 41. Beard JR, Si Y, Liu Z, Chenoweth L, Hanewald K. Intrinsic capacity: validation of a new WHO concept for healthy aging in a longitudinal Chinese study. J Gerontol A Biol Sci Med Sci. (2022) 77: 94 – 100. doi: doi:10.1093/gerona/glab226
  42. 42. Springer MV, Whitney RT, Ye W, Briceño EM, Gross AL, Aparicio HJ. Education levels and poststroke cognitive trajectories. JAMA Netw Open. (2025) 8: e252002. doi: doi:10.1001/jamanetworkopen.2025.2002
  43. 43. Leng Y, Knutson K, Carnethon MR, Yaffe K. Association between sleep quantity and quality in early adulthood with cognitive function in midlife. Neurology. (2024) 102: e208056. doi: doi:10.1212/WNL.0000000000208056
  44. 44. Tang SH, Lin JS, Wang YY, Qiao XX, Li YX. Path analysis of influencing factors on quality of life in stroke patients based on structural equation modeling. Chin J Nurs. (2023) 58: 2747 – 53. In Chinese.
  45. 45. Kim H, Teghipco A, Rorden C, Fridriksson J, Chaves M, Hillis AE. Hypoperfusion regions linked to National Institutes of Health Stroke Scale scores in acute stroke. Neuroimage Clin. (2025) 45: 103761. doi: doi:10.1016/j.nicl.2025.103761
  46. 46. Huang ZT, Lai ETC, Luo Y, Woo J. Social determinants of intrinsic capacity: a systematic review of observational studies. Ageing Res Rev. (2024) 95: 102239. doi: doi:10.1016/j.arr.2024.102239
  47. 47. Salinas-Rodríguez A, González-Bautista E, Rivera-Almaraz A, Manrique-Espinoza B. Longitudinal trajectories of intrinsic capacity and their association with quality of life and disability. Maturitas. (2022) 161: 49 – 54. doi: doi:10.1016/j.maturitas.2022.02.005
  48. 48. Ahmadi S, Afshar PF, Malakouti K, Azadbakht M. The relationship between intrinsic capacity and functional ability in older adults. BMC Geriatr. (2025) 25: 57. doi: doi:10.1186/s12877-025-05709-y
  49. 49. Wang S, Chen Y, Xiong L, Jin N, Zhao P, Liang Z. Multimorbidity measures associated with cognitive function among community-dwelling older Chinese adults. Alzheimers Dement. (2024) 20: 6221 – 31. doi: doi:10.1002/alz.14117
  50. 50. Liu-Ambrose T, Falck RS, Dao E, Best JR, Davis JC, Bennett K. Effect of exercise training or complex mental and social activities on cognitive function in adults with chronic stroke: a randomized clinical trial. JAMA Netw Open. (2022) 5: e2236510. doi: doi:10.1001/jamanetworkopen.2022.36510

Citation

Tang, X., et al. (2026). Latent class analysis of intrinsic capacity and its association with quality of life in hospitalized older people with stroke: a cross-sectional study. Frontiers in Public Health, Volume 14. https://doi.org/
Received
Accepted
Published
Volume
14 - 2026

Copyright

Copyright © 2026 Tang, Wu, Zhang, Tang and Yu.

License: https://creativecommons.org/licenses/by/4.0/

*Correspondence: Ling Yu

Article metrics

Views
0
Downloads
0
Citations
0
Shares
0

Related articles

Original ResearchBarriers and facilitators for sustainable fall prevention in a Chinese tertiary hospital: perspectives from nurses and managersXiang Chen, Mi Zhao, Zhenhui Tao, Bei Li, Dan Zhu, Jingwen Meng, Mengyun Yang, Ziye Huang, Chenchen Zhao, Chunyue Li, Qiaohong WangCorrectionCorrection: Pathogen prevalence, feature composition and cross-centre generalisability of machine learning diagnostic models for multi-pathogen respiratory infectionFengmiao Hu, Xingyu Zhou, Lijun Zhou, Zhirui Li, Shuang Dong, Chongkun XiaoMini ReviewThe impact of digitalisation on the development of physical activity of older people (through the example of European Union countries)Zhaxat Kenzhin, Tursynzada Kuangaliyeva, Assem Aitkaliyeva, Yerbol Myrzabossynov, Aigerim Zhunussova, Odiljon Dadabayev