Longitudinal study of Dyadic Self-Care in Stroke Patients and Caregivers: A Group-Based Multi-Trajectory Analysis

Longitudinal study of Dyadic Self-Care in Stroke Patients and Caregivers: A Group-Based Multi-Trajectory Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Longitudinal study of Dyadic Self-Care in Stroke Patients and Caregivers: A Group-Based Multi-Trajectory Analysis Wenna Wang, Beilei Lin, Pucciarelli Gianluca, Qiushi Zhang, Jing Cao, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7060592/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Background: Promoting self-care is widely recommended as an effective approach to reducing the burden of stroke. As a dyadic process, it involves the active participation of both patients and caregivers. However, its complexity and long-term nature are still not well understood. Objective: To investigate the developmental trajectories of dyadic self-care in stroke patients and caregivers, elucidate distinct trajectory patterns and their influencing factors. Design: A longitudinal multi-center study was conducted. Settings: Outpatient and community settings in China. Methods: A total of 214 stroke patient–caregiver dyads completed data collection at four time points: 1 month (T0), 3 months (T1), 6 months (T2), and 12 months (T3) following discharge after a first-ever stroke. Group-based multi-trajectory modeling was employed to identify the heterogeneity of the trajectories of the dyadic self-care maintenance, monitoring, and management among stroke patients and caregivers. Multiple logistic regression was used to explore the predictors of heterogeneous trajectories of dyadic self-care. Results: Four distinct trajectories of dyadic self-care maintenance were identified in stroke dyads: "Dyadic Middle-Low Decrease" (29.49%), "Patient Middle-Low Decrease and Caregiver Middle-High Decrease" (28.67%), "Patient Middle-High Decrease and Caregiver Middle-Low Decrease" (18.07%), and "Dyadic Middle-High Sustained" (23.77%). For dyadic self-care monitoring, the trajectories included: "Dyadic Middle-Low Decrease" (25.05%), "Patient Middle-Low Decrease and Caregiver Middle-High Decrease" (30.93%), "Patient Middle-High Decrease and Caregiver Middle-Low Decrease" (19.11%), and "Dyadic Middle-High Decrease" (24.91%). Dyadic self-care management trajectories comprised: "Dyadic Middle-Low Increase" (27.49%), "Patient Middle-Low Increase and Caregiver Middle-High Sustained" (25.43%), "Patient Middle-High Increase and Caregiver Middle-Low Decrease" (18.77%), and "Patient Middle-High Increase and Caregiver Middle-High Sustained" (28.31%). Multiple logistic regression analysis identified several significant predictors of dyadic self-care trajectories, including patients’ self-efficacy, mutuality, knowledge, stroke environment, use of electronic devices, employment and education status, as well as caregivers’ self-efficacy, mutuality, caregiving hours, relationship with the patient, knowledge, and employment status. Conclusions and Implications: The developmental trajectory of dyadic self-care for stroke patients and caregivers exhibits heterogeneity, suggesting that future research should integrate the longitudinal changes in dyadic self-care characteristics of patients and caregivers and trajectory classification, focusing on its influencing factors for precise classification and intervention. Registration: Clinical trial number: not applicable. Stroke Caregiver Dyads self-care Group-based multi-trajectory modelling Determinants Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 What is already known about the topic Stroke is the second leading cause of death and the third leading cause of death and disability worldwide, posing a serious threat to global health and well-being. Evidence suggests that effective self-care for stroke can lead to improved quality of life, while also reducing mortality rates, recurrence rate, hospital readmissions, and overall healthcare costs. Caregiver contributions to self-care play a crucial role in facilitating self-care of stroke patients, as stroke self-care is a dyadic process that involves both patients and caregivers. What this paper adds Significant changes over time were observed in the different dimensions of dyadic self-care (maintenance, monitoring, and management) for both stroke patients and caregivers. The developmental trajectory of dyadic self-care maintenance, monitoring, and management for stroke patients and caregivers exhibit heterogeneity. Factors influencing the development of dyadic self-care include patients' work status, daily living activity ability, and disease knowledge, self-efficacy, mutuality, stroke environment, as well as caregivers' work status, caregiving hours, relationship with the patient, disease knowledge, mutuality, and self-efficacy. 1. Introduction Stroke was the third leading cause of death and the fourth leading cause of disability-adjusted life years (DALYs) globally, with 93.8 million prevalent cases and 11.9 million new cases [ 1 ]. The prevalence of stroke reached 26 million in 2021, representing a 104.26% increase since 1990 in China. Furthermore, the number of DALYs attributable to stroke increased by 45.25% over the same period [ 2 ]. Despite receiving optimal treatment, patients with stroke still endure significant distress from various post-stroke functional impairments and recurrent strokes [ 3 ]. An increasing number of studies and practices have shown that promoting self-care is an effective strategy for enhancing recovery from stroke-related disabilities, reducing the risk of recurrence, improving health outcome [ 4 , 5 ]. According to the Middle-Range Theory of Self-Care of Chronic Illness, self-care in the context of chronic disease is conceptualized as a naturalistic decision-making process involving both the prevention and management of chronic conditions [ 6 ]. This process comprises three core components: self-care maintenance, self-care monitoring, and self-care management. The assistance provided by caregivers to patients is referred to as caregiver contributions to self-care and it plays a crucial role in supporting stroke patients throughout the self-care process [ 5 , 7 ]. Dyadic self-care in the context of chronic illness refers to the collaborative process in which patients and caregivers jointly engage in the patient's self-care, encompassing both the self-care of stroke patients and the caregiver contributions to self-care. The current level of dyadic self-care among chronic disease patients and their caregivers is relatively low, and more evidence is needed to support better engagement in stroke self-care [ 8 , 9 ]. Previous research has shown that dyadic self-care involving stroke patients and their caregivers is a complex process, influenced by a combination of patient-related factors, caregiver-related factors, dyadic interactions, and environmental conditions [ 10 ]. And enhancing dyadic self-care is essential not only for improving health outcomes in stroke patients but also for alleviating caregiver burden [ 11 , 12 ]. According to studies on chronic illness trajectory patterns and related literature, stroke exhibits a distinct disease progression trajectory [ 13 , 14 ]. Based on the characteristics of stroke, the illness trajectory typically includes four stages: the onset period (1–7 days post-stroke), the early rehabilitation period (1–8 weeks post-stroke), the sustained rehabilitation period (8 weeks to 6 months post-stroke), and the semi-stable period (6–12 months post-stroke). Each stage is associated with different characteristics and needs of both patients and their caregivers [ 15 ]. Meanwhile, self-care has been shown to be a dynamic behavioral adaptation process that evolves over time [ 16 ]. Pancani et al. conducted a six-month prospective longitudinal study and identified three distinct trajectories of self-care maintenance in patients with heart failure: (1) persistently poor, (2) marginal but improving, and (3) steadily improving [ 17 ]. Similarly, Dou et al. followed 119 patients with chronic obstructive pulmonary disease (COPD) over a six-month period and identified three self-care behavioral trajectories: (1) persistently declining, (2) slightly increasing and stable, and (3) gradually improving [ 18 ]. Qualitative studies have also demonstrated that caregivers’ contributions to self-care in chronic illness patients change in response to disease progression and other contextual factors [ 19 ]. However, there is currently a lack of longitudinal evidence regarding the trajectories of dyadic self-care in stroke patients and their caregivers. Prospective longitudinal research plays a critical role in deepening the understanding of how specific variables evolve over time. By continuously monitoring participants, researchers can observe temporal trends in the phenomena of interest and analyze their underlying causes and dynamics. Given that dyadic self-care between stroke patients and caregivers is a long-term and complex process, dynamic and empirical evidence is needed to guide targeted interventions. Therefore, applying a longitudinal research design to explore the developmental trajectories of dyadic self-care may provide important practical insights. 1.1. Aims The aims of this study were to: (i) explore the dyadic self-care development trajectories of stroke patients and their caregivers and identify different trajectory types; (i) examine the factors that influence dyadic self-care trajectories. 2. Methods 2.1. Design A prospective longitudinal design was employed in this study. 2.2. Data collection Data were collected from outpatient and community settings across Central China between October 2022 and December 2023. All investigators were registered nurses who had received standardized training. Participants were recruited using three main approaches: (i) investigators reviewed medical records to identify eligible patients, obtained consent, and conducted baseline surveys one month post-discharge at outpatient centers, patients’ homes, or via phone/WeChat, based on participant preference; (ii) investigators contacted patients by phone one month after discharge, explained the study, obtained consent, and conducted the baseline survey; (iii) patients returning for one-month follow-ups at outpatient centers were approached directly, and surveys were conducted after informed consent. Following the baseline survey, participants were followed up via phone at 3 (T1), 6 (T2), and 12 months (T3) post-discharge. Stroke patients and their primary caregivers were surveyed separately. If either party declined to participate, the case was considered lost to follow-up. 2.3. Sampling and Participants Potential participants in Henan Province were identified and recruited by researchers and healthcare providers. Stroke patients had to meet the following criteria: (i) a definitive stroke diagnosis, documented in medical records, and living at home for over one month post-discharge; (ii) aged 18 or older; (iii) willing to provide informed consent; (iv) no significant cognitive impairment (MMSE score ≥ 21) and able to answer questions clearly. Patients with other critical illnesses (e.g., respiratory failure, severe trauma), those entering rehabilitation institutions, or experiencing a stroke relapse during follow-up, as well as those requesting withdrawal, were excluded. Caregivers had to meet these criteria: (i) aged 18 or older, (ii) identified as the main informal carer responsible for patient care (e.g., spouse, child, parent), and (iii) willing to provide informed consent. Caregivers with serious psychiatric disorders, severe cognitive impairments, or those requesting withdrawal were excluded. 2.4. Instruments Demographics and clinical characteristics A questionnaire was used to obtain general information of stroke patients and caregivers. For stroke patients, the general information questionnaire includes details such as age, gender, educational level, marital status, current employment status, per capita monthly household income, type of stroke, use of electronic devices to learn about stroke information, and the patient's activities of daily living ability (Modified Barthel Index, MBI score). For caregivers, the general information questionnaire primarily covers age, gender, educational level, marital status, current employment status, relationship with the patient, daily caregiving duration, and use of electronic devices to learn about stroke information. Dyadic self-care : Self-care behaviors of stroke patients were measured with the Self-Care of Stroke Inventory (SCSI) developed in our previous study [ 20 ]. SCSI is a measure of self-care in stroke patients including 23 items divided into three separate scales: self-care maintenance, self-care monitoring and self-care management, each scale uses a five-point Likert scale per each item response, ranging from ‘never’ (1) to ‘always’ (5). Cronbach's αs ranges between 0.83 and 0.93 among the 3 scales 32. Reliability of the SCSI was excellent, with an intraclass correlation coefficient ranging between 0.83 and 0.94 among the 3 scales. A standardized score of 0 to 100 is calculated for each scale, with higher scores indicating better self-care behaviors of stroke. Caregiver contributions to self-care were measured with the Caregiver Contributions to Self-Care of Stroke Inventory (CCSCSI) developed in our previous study [ 21 ]. The 23-item CCSCSI comprises 3 scales which called caregiver contributions to self-care maintenance, caregiver contributions to self-care monitoring and caregiver contributions to self-care management, and uses a five-point Likert scale per each item response, ranging from ‘never’ (1) to ‘always’ (5). The Cronbach's α of the three scales range between 0.86 and 0.97 33. Each scale has a standardized score of 0 to 100, with higher scores indicating better caregiver contributions to self-care. Patient and caregiver disease knowledge The Stroke Health Knowledge Questionnaire was used to assess the disease knowledge of both patients and caregivers. This questionnaire was developed by Wan et al., to measure the level of knowledge related to the prevention of stroke [ 22 ]. The questionnaire contains a total of 25 items, with responses categorized as "Yes," "No," or "Uncertain." A score of 1 is awarded for a correct answer, and 0 is given for an incorrect or "Uncertain" answer. The standard score for each topic is calculated as the sum of the scores for the individual items divided by the total possible score for that topic, multiplied by 100. A higher standard score indicates a higher level of stroke health knowledge. The Cronbach's α of the questionnaire is 0.87, and the content validity is 0.89, indicating good reliability and validity of the scale. Patient and caregiver self-efficacy The Self-Care Self-Efficacy Scale (SCSES), consisting of 10 items, was used to assess self-care self-efficacy in stroke patients [ 23 ]. Each of the SCSES items begins with the question “How confident are you that you can ...”. Each item of the SCSES is rated on a scale of “1–5”, and the total score is converted to a standardized score with a maximum of 100 points, with higher scores being associated with higher self-efficacy. Cross-cultural validation of the SCSES in China (Hong Kong) has resulted in a Cronbach's α coefficient of 0.89. The 10-item Caregiver Self-Efficacy in Contributing to Patient Self-Care Scale (CSE-CSC) was adapted by from the SCSES to measure caregiver self-efficacy in facilitating patient self-care, with the same scoring system as the SCSES [ 24 ]. A significant correlation was found between the CSE-CSC and SCSES, indicating good reliability and validity of the CSE-CSC scale. In this study sample, the Cronbach’s α coefficient for the SCSES among stroke patients was 0.96, whereas for the CSE-CSC among stroke caregivers, it was 0.93. Patient and caregiver mutuality The 15-item Mutuality Scale (MS) was used to measure mutuality in stroke patients and their caregivers [ 25 ]. The MS elicits responses on a 5-point Likert-type scale (where 0 = not at all and 4 = a great deal). The scores for each dimension and for the total scale are computed by averaging the item scores in each dimension or in the total scale, with higher scores indicating better mutuality. The MS exhibits a four-factor structure (i.e., love, shared pleasurable activities, shared values, and reciprocity) in both the patient and caregiver versions. The intraclass correlations range from 0.66 to 0.93 in both the patient and caregiver versions of the MS. In this study sample, the Cronbach’s α coefficient for the MS was 0.95 for stroke patients and 0.96 for stroke caregivers. Stroke environment : The Measure of Stroke Environment (MOSE) was used to assess patients' perceptions of the stroke environment. This scale, developed by Babulal et al., includes three subscales: the acceptance environment, the built environment, and the communication environment [ 26 ]. It is designed to evaluate the environmental factors perceived by stroke patients as they reintegrate into the community. The scale uses a 4-point Likert scale to measure participants' perceptions of each item. The maximum score is 300, and higher scores indicate a more positive experience of the environment. The scale has been adapted into Chinese by Wang et al [ 27 ]. The total Cronbach’s α coefficient is 0.945, and the Cronbach’s α coefficients for each subscale range from 0.841 to 0.923, indicating good reliability and validity of the scale. 2.5. Ethical considerations This study was approved by the Research Ethics Committee of Zhengzhou University (ZZUIRB2021-115). Participation was voluntary, and participants were informed that they could withdraw from the study at any time without penalty. To ensure data security and maintain anonymity, each individual was assigned a unique identification code. All participants provided informed consent prior to participation. The study was conducted in accordance with the Declaration of Helsinki. 2.6. Data analyses SPSS version 25.0 was used to analyze the sociodemographic and clinical characteristics, while Stata 17.0 was employed to perform the Group-Based Multi-Trajectory Modeling. Determinants with a p-value < 0.05 (two-tailed) were considered statistically significant. The continuous data were presented as mean ± standard deviation, and categorical data were expressed as frequency and percentage. The Chi-square test and Fisher's exact probability test were used for categorical data, while the independent sample t-test was applied for continuous data to compare the differences in general characteristics between the participants who completed follow-up and those who were lost to follow-up. Repeated Measures ANOVA was used to compare the differences in dyadic self-care of stroke patients and the caregivers' contributions to self-care across different time points. The Group-Based Trajectory Model is a specialized statistical method based on finite mixture modeling, which identifies clusters of individuals with similar trajectories using Full Information Maximum Likelihood estimation [ 28 ]. The Group-Based Multi-Trajectory Modeling (GBmTM) is an extension of the GBTM [ 29 ]. This approach allows for the joint modeling of dyadic self-care scores for both stroke patients and caregivers, enabling the identification of groups of individuals with similar trajectories over time. In this study, model fit was evaluated using several criteria, including the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Sample-Size Adjusted BIC (SaBIC), Entropy, Average Posterior Probability (AvePP), and category probability. SaBIC, which adjusts for sample size, is more suitable for smaller samples. AIC, BIC, and SaBIC are indicators of model complexity and data fit, with lower values indicating better model fit. The Entropy value, closer to 1, indicates better model fit. AvePP evaluates the model’s ability to reflect the sample data, with values closer to 1 being better; typically, AvePP should not be less than 0.7. The minimum category probability refers to the smallest proportion of any category within the sample, related to the simplicity of the model, and is generally not lower than 5%. Considering the sample size and the specific circumstances of this study, the minimum category probability should ideally be no less than 10% when determining the model. In Stata statistical software, the fitting process began with fewer groups, initially fitting each trajectory group using higher-order functions (third-order polynomials). The AvePP for each group was then calculated, and the model fit was evaluated comprehensively to determine the best-fitting model. Multinomial logistic regression was used for multivariable analysis to identify key factors in each group, offering insights to inform clinical practice. 3. Results 3.1. Participant Characteristics and Follow-Up Figure 1 presents the flow diagram outlining the process of participant enrollment and follow-up during the study period. Of the 263 dyads initially screened, 49 dropped out after the baseline assessment. Among the 214 stroke patient-caregiver dyads who completed the full follow-up, the mean age of patients was 64.75 ± 10.21 years, with the majority being male (72.4%) and having a junior high school education (43.9%). Most patients were diagnosed with ischemic stroke (84.6%). Caregivers had a mean age of 56.91 ± 12.75 years, were predominantly female (72.9%), and most were spouses of the patients (72.9%). Comparison of baseline characteristics between the follow-up group and the lost-to-follow-up group revealed no statistically significant differences in patient or caregiver age, gender, education level, marital status, or employment status et al. ( P > 0.05), suggesting that the two groups were comparable. Details of these characteristics are provided in Tables 1 and 2 . Table 1 Comparison of characteristics between follow-up and lost-to-follow-up patients Variable Follow-up (n = 214) [n (%)] / (Mean ± SD) Lost to Follow-up (n = 49) [n (%)] / (Mean ± SD) Test Statistic P Age (years) 64.75 ± 10.21 62.35 ± 10.14 1.490 a 0.137 Sex 1.761 b 0.184 Male 155(72.4) 40(81.6) Female 59(21.6) 9(11.4) Education level 0.555 b 0.456 Junior high school or below 143(66.8) 30(61.2) High school or above 71(33.2) 19(38.8) Marital status 0.658 c 0.880 Unmarried 5(2.3) 1(2.0) Married 198(92.5) 47(96.0) Divorced/Widowed 11(5.2) 1(2.0) Employment status 0.465 b 0.792 Employed 27(12.6) 6(12.2) Unemployed/Medical leave 39(18.2) 11(22.5) Retired 148(69.2) 32(65.3) Monthly household income per capita (RMB) 1.758 b 0.624 < 1000 21(9.8) 8(16.3) 1000–2999 83(38.8) 17(34.7) 3000–4999 82(38.3) 18(36.7) ≥ 5000 28(13.1) 6(12.2) Stroke type 0.526 b 0.769 Ischemic 181(84.6) 40(81.6) Hemorrhagic 27(12.6) 8(16.4) Mixed 6(2.8) 1(2.0) Use of electronic devices to learn information about stroke 0.109 b 0.741 Yes 69(32.2) 17(34.7) No 145(67.8) 32(65.3) MBI score 76.68 ± 15.85 75.92 ± 16.698 0.301 a 0.763 Note: a = Independent samples t-test; b = Chi-square test; c = Fisher’s exact test. Table 2 Comparison of characteristics between follow-up and lost-to-follow-up caregivers Variable Follow-up (n = 214) [n (%)] / (Mean ± SD) Lost to Follow-up (n = 49) [n (%)] / (Mean ± SD) Test Statistic P Age (years) 56.91 ± 12.75 54.67 ± 14.13 1.086 a 0.279 Sex 3.520 b 0.061 Male 58(27.1) 7(14.3) Female 156(72.9) 42(85.7) Education level 2.583 b 0.108 Junior high school or below 131(61.2) 36(73.5) High school or above 83(38.8) 13(17.9) Marital status 1.567 c 0.465 Unmarried 13(6.1) 1(2.0) Married 196(91.6) 48(98.0) Divorced/Widowed 5(2.3) 0(0) Employment status 0.636 b 0.727 Employed 63(29.4) 12(24.5) Unemployed/Medical leave 56(26.2) 15(30.6) Retired 95(44.4) 22(44.9) Relationship to patient 0.043 b 0.835 Spouse 156(72.90) 35(71.40) Child/Parent/Relative/Friend 58(27.10) 14(28.60) Daily caregiving time 1.717 b 0.190 < 8h 131(61.20) 25(51.00) ≥ 8 h 83(38.80) 24(49.00) Use of electronic devices to learn information about stroke 0.250 b 0.617 Yes 78(36.4) 16(32.7) No 136(63.6) 33(67.3) Note: a = Independent samples t-test; b = Chi-square test; c = Fisher’s exact test. 3.2. Dyadic self-care scores over time in stroke patients and caregivers Patient self-care maintenance scores showed an initial increase followed by a decline from T0 (66.92 ± 15.02) to T3 (62.85 ± 16.14). One-way repeated measures ANOVA indicated a statistically significant overall change (P < 0.05). Pairwise comparisons showed significant differences between all time points (T0 vs. T1, T0 vs. T2, T0 vs. T3, T1 vs. T2, T1 vs. T3, T2 vs. T3; all P < 0.05). Patient self-care monitoring scores declined from T0 (67.91 ± 11.78) to T3 (57.18 ± 12.79), with a statistically significant overall trend (P < 0.05). All pairwise comparisons were statistically significant (P < 0.05). Patient self-care management scores increased from T0 (61.39 ± 15.63) to T3 (69.86 ± 17.35), with a significant overall trend (P < 0.05). Pairwise comparisons showed significant differences between all time points (P < 0.05). Caregiver contributions to self-care maintenance decreased from T0 (69.31 ± 14.33) to T3 (62.81 ± 17.48). ANOVA indicated a significant overall change (P < 0.05). No significant difference was found between T0 and T1 (P = 0.170), while all other comparisons were statistically significant (P < 0.05). Caregiver contributions to self-care monitoring decreased from T0 (70.23 ± 11.92) to T3 (59.52 ± 13.93), with a significant overall trend (P < 0.05). No significant difference was found between T0 and T1 (P = 0.549), while other comparisons showed statistical significance (P < 0.05). Caregiver contributions to self-care management showed an initial increase followed by a decline, from T0 (65.23 ± 16.34) to T3 (67.35 ± 16.41), with a statistically significant overall trend (P < 0.05). No significant differences were observed between T1 and T2 (P = 0.746) or T1 and T3 (P = 0.096), while other pairwise comparisons were significant (P < 0.05). For details, see Table 3 and Fig. 2 . Table 3 Scores of dyadic self-care in patients and caregivers at different time points (n = 214 dyads) Item T0 T1 T2 T3 F P Patient P-Maintenance 66.92 ± 15.02 68.39 ± 14.45 65.28 ± 15.48 62.85 ± 16.14 44.491 0.002 P-Monitoring 67.91 ± 11.78 66.24 ± 12.15 62.07 ± 13.08 57.18 ± 12.79 96.271 < 0.001 P-Management 61.39 ± 15.63 64.50 ± 15.28 67.55 ± 16.24 69.86 ± 17.35 27.943 < 0.001 Caregiver C-Maintenance 69.31 ± 14.33 68.65 ± 14.27 64.76 ± 16.11 62.81 ± 17.48 25.285 < 0.001 C-Monitoring 70.23 ± 11.92 68.13 ± 12.20 63.53 ± 13.04 59.52 ± 13.93 65.701 < 0.001 C-Management 65.23 ± 16.34 68.60 ± 15.57 68.44 ± 15.38 67.35 ± 16.41 21.954 < 0.001 Note: P-Maintenance = patient self-care maintenance; P-Monitoring = patient self-care monitoring; P-Management = patient self-care management; C-Maintenance = caregiver contribution to self-care maintenance; C-Monitoring = caregiver contribution to self-care monitoring; C-Management = caregiver contribution to self-care management. 3.3. Group-based multi-trajectory analysis of dyadic self-care 3.3.1 Dyadic self-care maintenance Using a group-based multi-trajectory model to analyze dyadic self-care maintenance scores in stroke patients and caregivers, model fit indices including AIC, BIC, SABIC, class probabilities, and AvePP values were evaluated. Models 5 and 6 were excluded due to class probabilities below 10% and suboptimal AvePP and entropy values. Among Models 1 to 4, the absolute values of AIC, BIC, and SABIC progressively decreased, reaching the lowest at the 4-class model, indicating the best fit. At this solution, the AvePP values for Groups 1 through 4 were 0.98, 0.96, 0.90, and 0.96, respectively. Detailed results are presented in Table 4 . Table 4 Grouping criteria for dyadic self-care maintenance trajectory model Number of Groups Polynomial AIC BIC SABIC Entropy Class Probability (%) 1 group 3 -7090.29 -7107.12 -7094.02 - - 2 groups 33 -6760.50 -6792.48 -6766.28 0.928 55.30/44.70 3 groups 333 -6615.41 -6662.54 -6623.25 0.922 37.55/20.27/42.18 4 groups 3333 -6515.46 -6577.73 -6525.34 0.910 29.25/18.27/28.10/24.38 5 groups 33333 -6480.80 -6558.22 -6492.73 0.899 7.22/26.94/19.44/25.90/20.50 6 groups 333333 -6459.52 -6552.09 -6473.50 0.913 2.96/27.01/19.50/18.84/22.51/9.18 Note: AIC = Akaike Information Criterion; BIC = Bayesian Information Criterion; SABIC = Sample-size Adjusted BIC; Entropy = classification accuracy index. When all trajectories for dyadic self-care maintenance in stroke patients and caregivers were modeled using cubic polynomials (3333/3333), the trajectories of patients and caregivers in Groups 1 through 4 were not statistically significant. Dimensionality reduction was applied stepwise to the non-significant trajectories, followed by multiple iterations. The optimal polynomial specification was identified as 1110/1110. Under this model, the AIC was − 6512.28, BIC was − 6544.26, and SABIC was − 6491.87. The AvePP values for Groups 1 to 4 were 0.97, 0.92, 0.96, and 0.97, respectively, with an entropy value of 0.907. The class probabilities were 29.49%, 28.67%, 18.07%, and 23.77%, and all group trajectories were statistically significant (P < 0.05), indicating good model fit. See Table 5 for details. Table 5 Optimal polynomial results for the dyadic self-care maintenance model Group Order Term Estimate SE t P Patient Group 1 1 Intercept 53.948 0.985 54.752 < 0.001 Linear slope -0.823 0.138 -5.948 < 0.001 Group 2 1 Intercept 66.311 1.292 51.318 < 0.001 Linear slope -0.434 0.143 -3.034 0.002 Group 3 1 Intercept 78.101 1.393 56.059 < 0.001 Linear slope -0.436 0.178 -2.447 < 0.001 Group 4 0 Intercept 81.261 0.913 89.039 < 0.001 Caregiver Group 1 1 Intercept 61.991 1.066 58.138 < 0.001 Linear slope -1.103 0.154 -7.138 < 0.001 Group 2 1 Intercept 75.302 1.611 46.750 < 0.001 Linear slope -0.326 0.162 -2.006 0.045 Group 3 1 Intercept 56.335 1.595 35.329 < 0.001 Linear slope -0.858 0.205 -4.185 < 0.001 Group 4 0 Intercept 82.114 0.808 101.687 < 0.001 Note:Group 1 = Dyadic Middle-Low Decrease Group; Group 2 = Patient Middle-Low Decrease and Caregiver Middle-High Decrease Group; Group 3 = Patient Middle-High Decrease and Caregiver Middle-Low Decrease Group; Group 4 = Dyadic Middle-High Sustained Group Based on prior findings, a self-care score above 70 indicates a high level of self-care in stroke patients, and a caregiver contribution score above 70 indicates a high level of caregiver involvement. Using this threshold to classify trajectory patterns, the four groups were named as follows: the "Dyadic Middle-Low Decrease Group" (29.49%), the "Patient Middle-Low Decrease and Caregiver Middle-High Decrease Group" (28.67%), the "Patient Middle-High Decrease and Caregiver Middle-Low Decrease Group" (18.07%), and the "Dyadic Middle-High Sustained Group" (23.77%). See Fig. 3 . 3.3.2 Dyadic self-care monitoring Based on model fit indicators such as AIC, BIC, SABIC, category probabilities, and AvePP values, along with clinical relevance, the 4-group model was determined to be the optimal choice for dyadic self-care monitoring trajectory. At this point, the AvePP values for Group 1, Group 2, Group 3, and Group 4 were 0.95, 0.92, 0.92, and 0.95, respectively. The grouping criteria for the dyadic self-care monitoring trajectory model for stroke patients and caregivers are detailed in Table 6 . Table 6 Grouping criteria for dyadic self-care monitoring trajectory model Number of Groups Polynomial AIC BIC SABIC Entropy Class Probability (%) 1 Group 3 -6768.43 -6785.26 -6773.07 - - 2 Groups 33 -6473.60 -6505.58 -6481.20 0.897 38.86/61.14 3 Groups 333 -6358.09 -6405.21 -6368.65 0.899 31.80/20.32/47.88 4 Groups 3333 -6255.72 -6317.99 -6269.24 0.890 25.10/31.29/18.97/24.63 5 Groups 33333 -6205.14 -6282.55 -6221.61 0.897 9.70/23.37/17.88/25.79/23.26 6 Groups 333333 -6294.97 -6387.54 -6314.41 0.906 6.79/3.43/22.85/17.82/25.97/23.14 Note:AIC = Akaike Information Criterion; BIC = Bayesian Information Criterion; SABIC = Sample-size Adjusted BIC; Entropy = classification accuracy index. When the three-order terms (3333/3333) were applied to the dyadic self-care monitoring trajectories of stroke patients and caregivers, the statistical results for the trajectories in Groups 1, 2, 3, and 4 were not significant. After performing stepwise dimensionality reduction on the non-significant trajectories, multiple iterations were conducted to optimize the model. The final optimal polynomial was 2111/1111, with AIC = -6250.34, BIC = -6287.37, SABIC = -6238.62, and AvePP values of 0.95, 0.92, 0.92, and 0.95 for Groups 1, 2, 3, and 4, respectively. The Entropy index was 0.887, and the category probabilities for each group were 25.05%, 30.93%, 19.11%, and 24.91%, with all trajectories showing significant statistical significance (all P < 0.05), indicating a good model fit, as shown in Table 7 . Table 7 Optimal polynomial results for the dyadic self-care monitoring model Group Order Term Estimate SE t P Patient Group 1 2 Intercept 57.345 1.490 38.476 < 0.001 Linear slope -2.015 0.589 -3.420 < 0.001 Second-order slope 0.073 0.042 2.014 0.047 Group 2 2 Intercept 66.226 1.078 61.406 < 0.001 Linear slope -1.063 0.123 -8.612 < 0.001 Group 3 1 Intercept 76.729 1.253 61.257 < 0.001 Linear slope -1.075 0.160 -6.727 < 0.001 Group 4 1 Intercept 79.593 0.945 84.247 < 0.001 Linear slope -0.797 0.137 -5.835 < 0.001 Caregiver Group 1 2 Intercept 60.376 1.149 52.533 < 0.001 Linear slope -1.279 0.145 -8.811 < 0.001 Group 2 1 Intercept 76.375 1.149 66.453 < 0.001 Linear slope -1.010 0.142 -7.126 < 0.001 Group 3 1 Intercept 61.725 1.257 49.107 < 0.001 Linear slope -0.895 0.174 -5.160 < 0.001 Group 4 1 Intercept 81.394 1.072 75.918 < 0.001 Linear slope -0.714 0.140 -5.089 < 0.001 Note:AIC = Akaike Information Criterion; BIC = Bayesian Information Criterion; SABIC = Sample-size Adjusted BIC; Entropy = classification accuracy index. Based on the distribution characteristics of each group, Group 1 was named the "Dyadic Middle-Low Decrease Group" (25.05%), Group 2 was named the "Patient Middle-Low Decrease and Caregiver Middle-High Decrease Group" (30.93%), Group 3 was named the "Patient Middle-High Decrease and Caregiver Middle-Low Decrease Group" (19.11%), and Group 4 was named the "Dyadic Middle-High Decrease Group" (24.91%). As shown in Fig. 4 . 3.3.3 Dyadic self-care management Based on model fit indicators such as AIC, BIC, SABIC, category probabilities, and AvePP values, as well as clinical significance, four groups were determined to be the optimal choice for the dyadic self-care management model. At this point, the AvePP values for Groups 1, 2, 3, and 4 were 0.99, 0.93, 0.97, and 0.97, respectively. The grouping criteria for the dyadic self-care management trajectory model are detailed in Table 8 . Table 8 Grouping criteria for dyadic self-care management trajectory model Number of Groups Polynomial AIC BIC SABIC Entropy Class Probability (%) 1 Group 3 -7146.83 -7163.66 -7151.47 - - 2 Groups 33 -6820.25 -6852.22 -6827.84 0.905 52.64/47.36 3 Groups 333 -6614.69 -6661.82 -6625.26 0.938 39.10/19.99/40.91 4 Groups 3333 -6511.70 -6573.97 -6525.22 0.932 25.31/19.02/27.18/28.48 5 Groups 33333 -6456.99 -6534.41 -6473.47 0.924 23.19/22.07/28.08/17.88/8.78 6 Groups 333333 -6445.76 -6538.33 -6465.20 0.930 20.14/22.34/16.99/16.18/22.48/1.87 Note:AIC = Akaike Information Criterion; BIC = Bayesian Information Criterion; SABIC = Sample-size Adjusted BIC; Entropy = classification accuracy index. To optimize the model, stepwise dimensionality reduction was applied to trajectories with non-significant statistical results, followed by multiple iterations. Ultimately, the best polynomial for the trajectories was 1121/1010. At this point, the model's AIC was − 6507.66, BIC was − 6541.32, and SABIC was − 6492.57. The AvePP values for Groups 1, 2, 3, and 4 were 0.97, 0.97, 0.94, and 0.96, respectively. The Entropy index was 0.930, and the category probabilities for each group were 27.49%, 25.43%, 18.77%, and 28.31%. All groups had statistically significant trajectories (all P < 0.05), indicating good model fit, as shown in Table 9 . Table 9 Optimal polynomial results for the dyadic self-care management model Group Order Term Estimate SE t P Patient Group 1 2 Intercept 45.210 1.056 42.821 < 0.001 Linear slope 0.362 0.158 2.290 0.022 Group 2 2 Intercept 57.575 1.185 48.586 < 0.001 Linear slope 0.695 0.156 4.440 < 0.001 Group 3 1 Intercept 63.162 2.173 29.068 < 0.001 Linear slope 3.593 0.803 4.471 < 0.001 Second-order slope -0.176 0.058 -3.045 0.002 Group 4 1 Intercept 77.045 1.046 73.684 < 0.001 Linear slope 0.795 0.144 5.513 < 0.001 Caregiver Group 1 2 Intercept 52.672 1.270 41.467 < 0.001 Linear slope 0.770 0.171 4.513 < 0.001 Group 2 1 Intercept 73.676 0.802 91.912 < 0.001 Group 3 1 Intercept 53.505 1.616 33.111 < 0.001 Linear slope -0.627 0.202 -3.109 0.002 Group 4 1 Intercept 82.827 0.753 109.996 < 0.001 Note:AIC = Akaike Information Criterion; BIC = Bayesian Information Criterion; SABIC = Sample-size Adjusted BIC; Entropy = classification accuracy index. Based on the distribution characteristics of each group, Group 1 is named the "Dyadic Middle-Low Increase Group" (27.49%), Group 2 is named the "Patient Middle-Low Increase and Caregiver Middle-High Sustained Group" (25.43%), Group 3 is named the "Patient Middle-High Increase and Caregiver Middle-Low Decrease Group" (18.77%), and Group 4 is named the "Patient Middle-High Increase and Caregiver Middle-High Sustained Group" (28.31%). Refer to Fig. 5 . 3.4. Predictors of heterogeneous trajectories of dyadic self-care Multinomial logistic regression was performed with the dyadic self-care maintenance trajectory groups as the dependent variable. Predictors included patient and caregiver characteristics found significant in univariate analysis: patient education, ADL capability; caregiver gender, relationship to the patient, and daily caregiving time. Additional variables comprised disease knowledge, self-efficacy, mutuality, and perceived stroke environment for both parties. Group 4 ("Dyadic Middle-High Sustained Group") served as the reference.Compared to Group 4, membership in Group 1 ("Dyadic Middle-Low Decrease Group") was more likely when patients reported lower self-efficacy and poorer stroke environment, and caregivers were non-spousal, provided care < 8 hours/day, and had lower self-efficacy (all P < 0.05). Group 2 ("Patient Middle-Low Sustained and Caregiver Middle-High Sustained Group") was associated with patients having low self-efficacy and poor mutuality, and caregivers providing ≥ 8 hours/day of care (all P < 0.05). Group 3 ("Patient Middle-High Sustained and Caregiver Middle-Low Decrease Group") was more likely when caregivers had limited stroke knowledge, lower self-efficacy, and caregiving time < 8 hours/day (all P < 0.05). The dyadic self-care monitoring trajectory served as the dependent variable in a separate multinomial logistic regression. Independent variables included statistically significant patient and caregiver factors from univariate analysis: patient education, employment status, electronic device usage for stroke learning, and ADL; caregiver employment and device usage. Also included were both parties' disease knowledge, self-efficacy, mutuality, and stroke environment. Group 4 was used as the reference. Patients not using electronic devices, with low knowledge, low self-efficacy, and negative perceptions of the stroke environment, were more likely to be in Group 1 ("Dyadic Middle-Low Decrease Group") (all P < 0.05). Group 2 ("Patient Middle-Low Decrease and Caregiver Middle-High Decrease Group") was associated with low patient disease knowledge and self-efficacy (P < 0.05). Group 3 ("Patient Middle-High Decrease and Caregiver Middle-Low Decrease Group") was linked to unemployed patients with high ADL scores and negative stroke environment, and caregivers who were also unemployed/on leave, with low self-efficacy and mutuality (all P < 0.05). For the self-care management trajectory, a multinomial logistic model was constructed. Predictors included significant variables from univariate tests: patient education, ADL ability, use of electronic devices; caregiver-patient relationship, and device use. Additional variables included both parties’ disease knowledge, self-efficacy, mutuality, and stroke environment. Group 4 served as the reference. Patients who did not use electronic devices, and had lower knowledge, self-efficacy, and perceived environmental support—along with caregivers reporting low self-efficacy—were more likely to belong to Group 1 ("Dyadic Middle-Low Increase Group") (P < 0.05). Group 2 ("Patient Middle-Low Increase and Caregiver Middle-High Sustained Group") was associated with lower patient education, self-efficacy, mutuality, and stroke environment, and lower caregiver self-efficacy (P < 0.05). Group 3 ("Patient Middle-High Increase and Caregiver Middle-Low Decrease Group") was more likely when patients did not use electronic devices, had good ADL function but poor environmental perception, and caregivers had lower self-efficacy (P < 0.05). 4. Discussion 4.1 Dyadic Self-Care Levels and Development Trends The results of this study indicate that stroke patients and their caregivers demonstrate significant room for improvement in dyadic self-care behaviors—including maintenance, monitoring, and management—within the first year post-discharge (all scores ≤ 70). These findings align with Locatelli et al., who reported similarly low levels of caregiver contribution to self-care in heart failure patients during a one-year follow-up [ 30 , 31 ]. This highlights the persistent challenges in enhancing dyadic self-care in chronic disease contexts. In this study, patients’ self-care maintenance and caregivers’ contributions to maintenance behaviors showed an increasing trend from T0 to T1 (three months post-discharge), followed by a decline at T3. This differs from Pancani et al., whose study on 225 heart failure patients did not observe this pattern [ 17 ]. One possible explanation is that daily health behaviors, health literacy, and treatment adherence were gradually neglected or forgotten by both patients and caregivers after the initial three-month period. Additionally, our findings suggest that caregivers’ contributions to maintenance behaviors tend to diminish over time, indicating that targeted caregiver support or training may be particularly beneficial within the first three months post-discharge. Self-care monitoring behaviors by patients and caregivers exhibited a continuous decline from T0 (one month post-discharge) onward, suggesting low compliance in tracking symptoms and treatment responses, which deteriorated further over time. These findings underscore the urgent need for early interventions—ideally within the first month post-discharge—focused on improving self-care monitoring and ensuring ongoing evaluation to refine such interventions. In terms of management behaviors, patients demonstrated a rapid increase following discharge, which then stabilized. However, caregiver contributions declined at T2, indicating that as patients develop self-management capacity, caregivers may reduce their involvement. These dynamic patterns call for nuanced, time-sensitive interventions that address different phases of the dyadic self-care process. Recent longitudinal studies in other chronic conditions—such as coronary heart disease and multimorbidity—have also begun to explore dyadic self-care, which is crucial for the development of tailored theoretical models and intervention strategies in chronic disease management [ 19 , 32 ]. 4.2. Trajectory Groupings and Characteristics in Dyadic Self-Care Development Group-based trajectory modeling revealed four distinct developmental trajectories for dyadic self-care in maintenance, monitoring, and management domains: (1) moderately low dyadic trajectory, (2) patient moderately low–caregiver moderately high discordant trajectory, (3) patient moderately high–caregiver moderately low discordant trajectory, and (4) moderately high dyadic trajectory. These results indicate that dyadic self-care behaviors evolve over time and exhibit substantial heterogeneity across the first year post-discharge. Future interventions should aim to identify these distinct patterns and implement stratified strategies accordingly. Prior research has identified similar patterns in other populations. For instance, Kim et al. found three trajectories of self-care behaviors among heart failure patients: "low-decreasing" (20.9%), "moderate-increasing" (58.9%), and "high-stable" (20.2%) [ 33 ]. Son et al. identified "high-stable" and "low-persistent" groups in a one-year follow-up of 137 heart failure patients [ 34 ]. Dou et al. followed 119 patients with chronic obstructive pulmonary disease and reported three trajectories: "continuously declining", "slightly increasing and stable", and "gradually improving" [ 18 ]. These findings differ from our study, possibly due to variations in disease type, follow-up duration, and patient characteristics. Nevertheless, they highlight the need for precision-based interventions targeting specific dyadic self-care trajectories in stroke populations. This study offers a novel perspective by recognizing the potential discordance in dyadic self-care development between patients and caregivers. For example, in the “patient moderately low–caregiver moderately high” trajectory, patients’ self-care remained low despite high caregiver engagement. This may reflect conflicts or misalignment in patient–caregiver perspectives. Incorporating relational and communication-focused interventions into dyadic self-care programs may help close this gap and enhance outcomes for both parties [ 35 ]. Patients and caregivers classified in the “moderately low dyadic” group are at high risk for persistent self-care deficits and should be prioritized for more intensive and targeted interventions. In contrast, those in the “patient moderately high–caregiver moderately low” or “moderately high dyadic” trajectories may require only minimal professional guidance. Self-help-based approaches, delivered through books, videos, websites, or digital tools, may support these dyads in independently strengthening their self-care capabilities [ 36 ]. 4.3. Factors Influencing Dyadic Self-Care Trajectories Multinomial logistic regression analysis identified several key factors influencing dyadic self-care trajectories. For patients, educational attainment, employment status, use of digital tools to learn about stroke, functional ability in daily living, disease-related knowledge, and self-efficacy were significant predictors. These findings suggest that future interventions should prioritize enhancing patients’ functional independence, building digital literacy, and expanding access to digital health resources [ 37 , 38 ]. Moreover, training focused on disease knowledge and improving self-efficacy may facilitate more favorable self-care development trajectories [ 39 ]. Caregiver-related factors included the relationship to the patient, employment status, hours spent caregiving daily, disease knowledge, and self-efficacy. De Maria et al. also found that non-spousal caregivers were more likely to fall into less favorable dyadic maintenance trajectories, supporting our findings [ 40 ]. The process of becoming a well-informed, confident caregiver, termed caregiver activation, can positively influence dyadic self-care [ 41 ]. Future studies should integrate caregiver activation strategies into dyadic interventions, ensuring that caregivers are empowered to contribute meaningfully to patients’ self-care. The interdependence between patients and caregivers plays a crucial role in shaping dyadic self-care trajectories. This aligns with dyadic illness management theory, which emphasizes the mutual dependence of patient–caregiver dyads [ 42 ]. For example, patients with lower interdependence were more likely to fall into the “patient moderately low–caregiver moderately high” trajectory for self-care maintenance and management. In contrast, low caregiver interdependence was associated with the “patient moderately high–caregiver moderately low” trajectory in self-care monitoring. These patterns indicate that enhancing relational interdependence may steer dyads toward more optimal self-care outcomes. Therefore, interventions should include components that foster emotional closeness and collaborative partnership between patients and caregivers [ 10 , 43 ]. Finally, the patient's perception of environmental support significantly influenced the trajectories of self-care monitoring and management. Patients in more supportive environments were more likely to engage actively in managing their condition, supporting findings from prior qualitative reviews [ 44 ]. This highlights the importance of identifying and improving environmental factors, such as home and community infrastructure, that may otherwise hinder dyadic self-care development [ 45 ]. 4.4. Implications for practice and research This study highlights the heterogeneous and dynamic nature of dyadic self-care behaviors among stroke patients and their caregivers within the first year post-discharge. Early and continuous support, particularly within the first three months, is crucial. In clinical practice, targeted and stratified interventions should be developed based on distinct dyadic self-care trajectories (e.g., low-low, discordant types). Enhancing the quality of the dyadic relationship is essential to foster collaboration between patients and caregivers. Environmental support should also be optimized through community and home-based resources. Future research should further explore the mechanisms underlying dyadic self-care changes and develop personalized, technology-assisted interventions to improve accessibility and effectiveness. 4.5. Limitations This study has several limitations that should be considered when interpreting the findings. Firstly, due to constraints in manpower, resources, and time, the sample was limited to 263 stroke patient-caregiver dyads, with 49 dyads lost to follow-up. The relatively small sample size and high attrition rate may limit the generalizability of the results and introduce potential bias. Secondly, the early stages of the study were conducted during the COVID-19 pandemic. Government-imposed restrictions and lockdown measures may have influenced the actual self-care behaviors of patients and caregivers, potentially affecting the ecological validity of the findings. Researchers should exercise caution when comparing these results to those of studies conducted under different public health conditions. Thirdly, patients with cognitive impairments were excluded from participation, which may restrict the applicability of the findings to the broader stroke population. Future research should consider including individuals with varying levels of cognitive function to develop comprehensive interventions that reflect the heterogeneity of stroke survivors. 5. Conclusion The developmental trajectory of dyadic self-care among stroke patients and their caregivers demonstrates significant heterogeneity and time-dependent variability across self-care maintenance, monitoring, and management behaviors. These findings highlight the importance of adopting a longitudinal and dyadic perspective in both clinical practice and research. Future studies should aim to identify distinct dyadic self-care trajectory subgroups and explore the key influencing factors, including individual characteristics, relationship dynamics, and environmental conditions. By enhancing digital literacy, strengthening the patient-caregiver relationship, and improving the care environment, it is possible to support more favorable dyadic self-care outcomes over time. Abbreviations DALYs: disability-adjusted life years. COPD: chronic obstructive pulmonary disease. MBI: Modified Barthel Index. SCSI: the Self-Care of Stroke Inventory. CCSCSI: the Caregiver Contributions to Self-Care of Stroke Inventory. SCSES: the Self-Care Self-Efficacy Scale. CSE-CSC: the Caregiver Self-Efficacy in Contributing to Patient Self-Care Scale. MS: the Mutuality Scale. MOSE: the Measure of Stroke Environment. GBmTM: Group-Based Multi-Trajectory Modeling. AIC: Akaike Information Criterion. BIC: Bayesian Information Criterion. SaBIC: Sample-Size Adjusted BIC. Entropy: a measure of classification accuracy. AvePP: Average Posterior Probability. Declarations Ethics approval and consent to participate This study was approved by the Research Ethics Committee of Zhengzhou University (ZZUIRB2021-115). All participants provided informed consent prior to participation. The study was conducted in accordance with the Declaration of Helsinki. Consent for publication Not applicable. Availability of data and materials Data is provided within the manuscript or supplementary information files. Competing interests No conflict of interest has been declared by the authors. Declaration of Competing Interest The authors declare no conflicts of interest. Funding This study was supported by the National Natural Science Foundation of China [Grant No. 72174184], the Youth Science Fund of the National Natural Science Foundation of China [Grant No. 72004205], and the Henan Provincial Science and Technology Innovation Talent Program [Grant No. 134200510018]. 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1","display":"","copyAsset":false,"role":"figure","size":102121,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of participant enrollment and follow-up\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7060592/v1/e00ce26517363747c6327d1f.png"},{"id":93639140,"identity":"62fe09d8-4439-4d06-b3e0-4d6773ae3964","added_by":"auto","created_at":"2025-10-16 02:05:27","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":78474,"visible":true,"origin":"","legend":"\u003cp\u003eTrend in mean scores of dyadic self-care in stroke patients and caregivers\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7060592/v1/ee2440597782bb59cb2f5dc3.png"},{"id":93638320,"identity":"f82b92b3-c95e-4ddb-8a2e-490dbcefc45f","added_by":"auto","created_at":"2025-10-16 01:57:27","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":73342,"visible":true,"origin":"","legend":"\u003cp\u003eGroup Trajectories of Dyadic Self-Care Maintenance\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote. \u003c/strong\u003eP-SC-Maintenance = Patient Self-care Maintenance; CC-SC-Maintenance = Caregiver Contribution to Self-care Maintenance\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7060592/v1/de7a5ea121911a21dbe3cd79.png"},{"id":93638323,"identity":"06d5e880-a3c6-4220-80d3-7c11ddc11019","added_by":"auto","created_at":"2025-10-16 01:57:27","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":77356,"visible":true,"origin":"","legend":"\u003cp\u003eGroup Trajectories of Dyadic Self-Care Monitoring\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote. \u003c/strong\u003eP-SC-Monitoring = Patient Self-care Monitoring; CC-SC-Monitoring = Caregiver Contribution to Self-care Monitoring\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7060592/v1/9e16afac4e9c5f778763b481.png"},{"id":93639142,"identity":"011feb79-7025-4f9f-892a-0908a334534a","added_by":"auto","created_at":"2025-10-16 02:05:27","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":70474,"visible":true,"origin":"","legend":"\u003cp\u003eGroup Trajectories of Dyadic Self-Care management\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote. \u003c/strong\u003eP-SC- management = Patient Self-care management; CC-SC- management = Caregiver Contribution to Self-care management\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7060592/v1/7ca415e7d92dacdb5c496d5b.png"},{"id":93639863,"identity":"1377ba82-3e2e-4c85-a86f-6e364960fa3b","added_by":"auto","created_at":"2025-10-16 02:21:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2118330,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7060592/v1/a8580735-a763-4b9e-b398-e5525f456fea.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Longitudinal study of Dyadic Self-Care in Stroke Patients and Caregivers: A Group-Based Multi-Trajectory Analysis","fulltext":[{"header":"What is already known about the topic","content":"\u003cul\u003e\n \u003cli\u003eStroke is the second leading cause of death and the third leading cause of death and disability worldwide, posing a serious threat to global health and well-being.\u003c/li\u003e\n \u003cli\u003eEvidence suggests that effective self-care for stroke can lead to improved quality of life, while also reducing mortality rates, recurrence rate, hospital readmissions, and overall healthcare costs.\u003c/li\u003e\n \u003cli\u003eCaregiver contributions to self-care play a crucial role in facilitating self-care of stroke patients, as stroke self-care is a dyadic process that involves both patients and caregivers.\u0026nbsp;\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eWhat this paper adds\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eSignificant changes over time were observed in the different dimensions of dyadic self-care (maintenance, monitoring, and management) for both stroke patients and caregivers.\u003c/li\u003e\n \u003cli\u003eThe developmental trajectory of dyadic self-care maintenance, monitoring, and management for stroke patients and caregivers exhibit heterogeneity.\u003c/li\u003e\n \u003cli\u003eFactors influencing the development of dyadic self-care include patients\u0026apos; work status, daily living activity ability, and disease knowledge, self-efficacy, mutuality, stroke environment, as well as caregivers\u0026apos; work status, caregiving hours, relationship with the patient, disease knowledge, mutuality, and self-efficacy.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"1. Introduction","content":"\u003cp\u003eStroke was the third leading cause of death and the fourth leading cause of disability-adjusted life years (DALYs) globally, with 93.8\u0026nbsp;million prevalent cases and 11.9\u0026nbsp;million new cases [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The prevalence of stroke reached 26\u0026nbsp;million in 2021, representing a 104.26% increase since 1990 in China. Furthermore, the number of DALYs attributable to stroke increased by 45.25% over the same period [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Despite receiving optimal treatment, patients with stroke still endure significant distress from various post-stroke functional impairments and recurrent strokes [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. An increasing number of studies and practices have shown that promoting self-care is an effective strategy for enhancing recovery from stroke-related disabilities, reducing the risk of recurrence, improving health outcome [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAccording to the Middle-Range Theory of Self-Care of Chronic Illness, self-care in the context of chronic disease is conceptualized as a naturalistic decision-making process involving both the prevention and management of chronic conditions [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. This process comprises three core components: self-care maintenance, self-care monitoring, and self-care management. The assistance provided by caregivers to patients is referred to as caregiver contributions to self-care and it plays a crucial role in supporting stroke patients throughout the self-care process [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Dyadic self-care in the context of chronic illness refers to the collaborative process in which patients and caregivers jointly engage in the patient's self-care, encompassing both the self-care of stroke patients and the caregiver contributions to self-care. The current level of dyadic self-care among chronic disease patients and their caregivers is relatively low, and more evidence is needed to support better engagement in stroke self-care [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Previous research has shown that dyadic self-care involving stroke patients and their caregivers is a complex process, influenced by a combination of patient-related factors, caregiver-related factors, dyadic interactions, and environmental conditions [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. And enhancing dyadic self-care is essential not only for improving health outcomes in stroke patients but also for alleviating caregiver burden [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAccording to studies on chronic illness trajectory patterns and related literature, stroke exhibits a distinct disease progression trajectory [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Based on the characteristics of stroke, the illness trajectory typically includes four stages: the onset period (1\u0026ndash;7 days post-stroke), the early rehabilitation period (1\u0026ndash;8 weeks post-stroke), the sustained rehabilitation period (8 weeks to 6 months post-stroke), and the semi-stable period (6\u0026ndash;12 months post-stroke). Each stage is associated with different characteristics and needs of both patients and their caregivers [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Meanwhile, self-care has been shown to be a dynamic behavioral adaptation process that evolves over time [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\u003cp\u003ePancani et al. conducted a six-month prospective longitudinal study and identified three distinct trajectories of self-care maintenance in patients with heart failure: (1) persistently poor, (2) marginal but improving, and (3) steadily improving [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Similarly, Dou et al. followed 119 patients with chronic obstructive pulmonary disease (COPD) over a six-month period and identified three self-care behavioral trajectories: (1) persistently declining, (2) slightly increasing and stable, and (3) gradually improving [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Qualitative studies have also demonstrated that caregivers\u0026rsquo; contributions to self-care in chronic illness patients change in response to disease progression and other contextual factors [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. However, there is currently a lack of longitudinal evidence regarding the trajectories of dyadic self-care in stroke patients and their caregivers. Prospective longitudinal research plays a critical role in deepening the understanding of how specific variables evolve over time. By continuously monitoring participants, researchers can observe temporal trends in the phenomena of interest and analyze their underlying causes and dynamics. Given that dyadic self-care between stroke patients and caregivers is a long-term and complex process, dynamic and empirical evidence is needed to guide targeted interventions. Therefore, applying a longitudinal research design to explore the developmental trajectories of dyadic self-care may provide important practical insights.\u003c/p\u003e\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003ch2\u003e1.1. Aims\u003c/h2\u003e\u003cp\u003eThe aims of this study were to: (i) explore the dyadic self-care development trajectories of stroke patients and their caregivers and identify different trajectory types; (i) examine the factors that influence dyadic self-care trajectories.\u003c/p\u003e\u003c/div\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Design\u003c/h2\u003e\u003cp\u003eA prospective longitudinal design was employed in this study.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Data collection\u003c/h2\u003e\u003cp\u003eData were collected from outpatient and community settings across Central China between October 2022 and December 2023. All investigators were registered nurses who had received standardized training. Participants were recruited using three main approaches: (i) investigators reviewed medical records to identify eligible patients, obtained consent, and conducted baseline surveys one month post-discharge at outpatient centers, patients\u0026rsquo; homes, or via phone/WeChat, based on participant preference; (ii) investigators contacted patients by phone one month after discharge, explained the study, obtained consent, and conducted the baseline survey; (iii) patients returning for one-month follow-ups at outpatient centers were approached directly, and surveys were conducted after informed consent. Following the baseline survey, participants were followed up via phone at 3 (T1), 6 (T2), and 12 months (T3) post-discharge. Stroke patients and their primary caregivers were surveyed separately. If either party declined to participate, the case was considered lost to follow-up.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Sampling and Participants\u003c/h2\u003e\u003cp\u003ePotential participants in Henan Province were identified and recruited by researchers and healthcare providers. Stroke patients had to meet the following criteria: (i) a definitive stroke diagnosis, documented in medical records, and living at home for over one month post-discharge; (ii) aged 18 or older; (iii) willing to provide informed consent; (iv) no significant cognitive impairment (MMSE score\u0026thinsp;\u0026ge;\u0026thinsp;21) and able to answer questions clearly. Patients with other critical illnesses (e.g., respiratory failure, severe trauma), those entering rehabilitation institutions, or experiencing a stroke relapse during follow-up, as well as those requesting withdrawal, were excluded. Caregivers had to meet these criteria: (i) aged 18 or older, (ii) identified as the main informal carer responsible for patient care (e.g., spouse, child, parent), and (iii) willing to provide informed consent. Caregivers with serious psychiatric disorders, severe cognitive impairments, or those requesting withdrawal were excluded.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.4. Instruments\u003c/h2\u003e\u003cp\u003e\u003cstrong\u003eDemographics and clinical characteristics\u003c/strong\u003e\u003cp\u003eA questionnaire was used to obtain general information of stroke patients and caregivers. For stroke patients, the general information questionnaire includes details such as age, gender, educational level, marital status, current employment status, per capita monthly household income, type of stroke, use of electronic devices to learn about stroke information, and the patient's activities of daily living ability (Modified Barthel Index, MBI score). For caregivers, the general information questionnaire primarily covers age, gender, educational level, marital status, current employment status, relationship with the patient, daily caregiving duration, and use of electronic devices to learn about stroke information.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eDyadic self-care\u003c/b\u003e: Self-care behaviors of stroke patients were measured with the Self-Care of Stroke Inventory (SCSI) developed in our previous study [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. SCSI is a measure of self-care in stroke patients including 23 items divided into three separate scales: self-care maintenance, self-care monitoring and self-care management, each scale uses a five-point Likert scale per each item response, ranging from \u0026lsquo;never\u0026rsquo; (1) to \u0026lsquo;always\u0026rsquo; (5). Cronbach's αs ranges between 0.83 and 0.93 among the 3 scales 32. Reliability of the SCSI was excellent, with an intraclass correlation coefficient ranging between 0.83 and 0.94 among the 3 scales. A standardized score of 0 to 100 is calculated for each scale, with higher scores indicating better self-care behaviors of stroke. Caregiver contributions to self-care were measured with the Caregiver Contributions to Self-Care of Stroke Inventory (CCSCSI) developed in our previous study [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The 23-item CCSCSI comprises 3 scales which called caregiver contributions to self-care maintenance, caregiver contributions to self-care monitoring and caregiver contributions to self-care management, and uses a five-point Likert scale per each item response, ranging from \u0026lsquo;never\u0026rsquo; (1) to \u0026lsquo;always\u0026rsquo; (5). The Cronbach's α of the three scales range between 0.86 and 0.97 33. Each scale has a standardized score of 0 to 100, with higher scores indicating better caregiver contributions to self-care.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003ePatient and caregiver disease knowledge\u003c/strong\u003e\u003cp\u003eThe Stroke Health Knowledge Questionnaire was used to assess the disease knowledge of both patients and caregivers. This questionnaire was developed by Wan et al., to measure the level of knowledge related to the prevention of stroke [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The questionnaire contains a total of 25 items, with responses categorized as \"Yes,\" \"No,\" or \"Uncertain.\" A score of 1 is awarded for a correct answer, and 0 is given for an incorrect or \"Uncertain\" answer. The standard score for each topic is calculated as the sum of the scores for the individual items divided by the total possible score for that topic, multiplied by 100. A higher standard score indicates a higher level of stroke health knowledge. The Cronbach's α of the questionnaire is 0.87, and the content validity is 0.89, indicating good reliability and validity of the scale.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003ePatient and caregiver self-efficacy\u003c/strong\u003e\u003cp\u003eThe Self-Care Self-Efficacy Scale (SCSES), consisting of 10 items, was used to assess self-care self-efficacy in stroke patients [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Each of the SCSES items begins with the question \u0026ldquo;How confident are you that you can ...\u0026rdquo;. Each item of the SCSES is rated on a scale of \u0026ldquo;1\u0026ndash;5\u0026rdquo;, and the total score is converted to a standardized score with a maximum of 100 points, with higher scores being associated with higher self-efficacy. Cross-cultural validation of the SCSES in China (Hong Kong) has resulted in a Cronbach's α coefficient of 0.89. The 10-item Caregiver Self-Efficacy in Contributing to Patient Self-Care Scale (CSE-CSC) was adapted by from the SCSES to measure caregiver self-efficacy in facilitating patient self-care, with the same scoring system as the SCSES [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. A significant correlation was found between the CSE-CSC and SCSES, indicating good reliability and validity of the CSE-CSC scale. In this study sample, the Cronbach\u0026rsquo;s α coefficient for the SCSES among stroke patients was 0.96, whereas for the CSE-CSC among stroke caregivers, it was 0.93.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003ePatient and caregiver mutuality\u003c/strong\u003e\u003cp\u003eThe 15-item Mutuality Scale (MS) was used to measure mutuality in stroke patients and their caregivers [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The MS elicits responses on a 5-point Likert-type scale (where 0\u0026thinsp;=\u0026thinsp;not at all and 4\u0026thinsp;=\u0026thinsp;a great deal). The scores for each dimension and for the total scale are computed by averaging the item scores in each dimension or in the total scale, with higher scores indicating better mutuality. The MS exhibits a four-factor structure (i.e., love, shared pleasurable activities, shared values, and reciprocity) in both the patient and caregiver versions. The intraclass correlations range from 0.66 to 0.93 in both the patient and caregiver versions of the MS. In this study sample, the Cronbach\u0026rsquo;s α coefficient for the MS was 0.95 for stroke patients and 0.96 for stroke caregivers.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eStroke environment\u003c/b\u003e: The Measure of Stroke Environment (MOSE) was used to assess patients' perceptions of the stroke environment. This scale, developed by Babulal et al., includes three subscales: the acceptance environment, the built environment, and the communication environment [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. It is designed to evaluate the environmental factors perceived by stroke patients as they reintegrate into the community. The scale uses a 4-point Likert scale to measure participants' perceptions of each item. The maximum score is 300, and higher scores indicate a more positive experience of the environment. The scale has been adapted into Chinese by Wang et al [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The total Cronbach\u0026rsquo;s α coefficient is 0.945, and the Cronbach\u0026rsquo;s α coefficients for each subscale range from 0.841 to 0.923, indicating good reliability and validity of the scale.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.5. Ethical considerations\u003c/h2\u003e\u003cp\u003e This study was approved by the Research Ethics Committee of Zhengzhou University (ZZUIRB2021-115). Participation was voluntary, and participants were informed that they could withdraw from the study at any time without penalty. To ensure data security and maintain anonymity, each individual was assigned a unique identification code. All participants provided informed consent prior to participation. The study was conducted in accordance with the Declaration of Helsinki.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.6. Data analyses\u003c/h2\u003e\u003cp\u003eSPSS version 25.0 was used to analyze the sociodemographic and clinical characteristics, while Stata 17.0 was employed to perform the Group-Based Multi-Trajectory Modeling. Determinants with a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (two-tailed) were considered statistically significant.\u003c/p\u003e\u003cp\u003eThe continuous data were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, and categorical data were expressed as frequency and percentage. The Chi-square test and Fisher's exact probability test were used for categorical data, while the independent sample t-test was applied for continuous data to compare the differences in general characteristics between the participants who completed follow-up and those who were lost to follow-up. Repeated Measures ANOVA was used to compare the differences in dyadic self-care of stroke patients and the caregivers' contributions to self-care across different time points.\u003c/p\u003e\u003cp\u003eThe Group-Based Trajectory Model is a specialized statistical method based on finite mixture modeling, which identifies clusters of individuals with similar trajectories using Full Information Maximum Likelihood estimation [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The Group-Based Multi-Trajectory Modeling (GBmTM) is an extension of the GBTM [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. This approach allows for the joint modeling of dyadic self-care scores for both stroke patients and caregivers, enabling the identification of groups of individuals with similar trajectories over time.\u003c/p\u003e\u003cp\u003eIn this study, model fit was evaluated using several criteria, including the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Sample-Size Adjusted BIC (SaBIC), Entropy, Average Posterior Probability (AvePP), and category probability. SaBIC, which adjusts for sample size, is more suitable for smaller samples. AIC, BIC, and SaBIC are indicators of model complexity and data fit, with lower values indicating better model fit. The Entropy value, closer to 1, indicates better model fit. AvePP evaluates the model\u0026rsquo;s ability to reflect the sample data, with values closer to 1 being better; typically, AvePP should not be less than 0.7. The minimum category probability refers to the smallest proportion of any category within the sample, related to the simplicity of the model, and is generally not lower than 5%. Considering the sample size and the specific circumstances of this study, the minimum category probability should ideally be no less than 10% when determining the model. In Stata statistical software, the fitting process began with fewer groups, initially fitting each trajectory group using higher-order functions (third-order polynomials). The AvePP for each group was then calculated, and the model fit was evaluated comprehensively to determine the best-fitting model. Multinomial logistic regression was used for multivariable analysis to identify key factors in each group, offering insights to inform clinical practice.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1. Participant Characteristics and Follow-Up\u003c/h2\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e presents the flow diagram outlining the process of participant enrollment and follow-up during the study period. Of the 263 dyads initially screened, 49 dropped out after the baseline assessment. Among the 214 stroke patient-caregiver dyads who completed the full follow-up, the mean age of patients was 64.75\u0026thinsp;\u0026plusmn;\u0026thinsp;10.21 years, with the majority being male (72.4%) and having a junior high school education (43.9%). Most patients were diagnosed with ischemic stroke (84.6%). Caregivers had a mean age of 56.91\u0026thinsp;\u0026plusmn;\u0026thinsp;12.75 years, were predominantly female (72.9%), and most were spouses of the patients (72.9%). Comparison of baseline characteristics between the follow-up group and the lost-to-follow-up group revealed no statistically significant differences in patient or caregiver age, gender, education level, marital status, or employment status et al. (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), suggesting that the two groups were comparable. Details of these characteristics are provided in Tables \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eComparison of characteristics between follow-up and lost-to-follow-up patients\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFollow-up (n\u0026thinsp;=\u0026thinsp;214)\u003c/p\u003e\n \u003cp\u003e[n (%)] / (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLost to Follow-up (n\u0026thinsp;=\u0026thinsp;49)\u003c/p\u003e\n \u003cp\u003e[n (%)] / (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTest Statistic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64.75\u0026thinsp;\u0026plusmn;\u0026thinsp;10.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.35\u0026thinsp;\u0026plusmn;\u0026thinsp;10.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.490\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.137\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.761\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.184\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e155(72.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40(81.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59(21.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9(11.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducation level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.555\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.456\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJunior high school or below\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e143(66.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30(61.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh school or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71(33.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19(38.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarital status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.658\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.880\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnmarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5(2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e198(92.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47(96.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDivorced/Widowed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11(5.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmployment status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.465\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.792\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27(12.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6(12.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnemployed/Medical leave\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39(18.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11(22.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRetired\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e148(69.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32(65.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMonthly household income per capita (RMB)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.758\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.624\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;1000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21(9.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8(16.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1000\u0026ndash;2999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83(38.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17(34.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3000\u0026ndash;4999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82(38.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18(36.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;5000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28(13.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6(12.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStroke type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.526\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.769\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIschemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e181(84.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40(81.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHemorrhagic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27(12.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8(16.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMixed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6(2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUse of electronic devices to learn information about stroke\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.109\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.741\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69(32.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17(34.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e145(67.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32(65.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMBI score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76.68\u0026thinsp;\u0026plusmn;\u0026thinsp;15.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75.92\u0026thinsp;\u0026plusmn;\u0026thinsp;16.698\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.301\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.763\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eNote: a\u0026thinsp;=\u0026thinsp;Independent samples t-test; b\u0026thinsp;=\u0026thinsp;Chi-square test; c\u0026thinsp;=\u0026thinsp;Fisher\u0026rsquo;s exact test.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eComparison of characteristics between follow-up and lost-to-follow-up caregivers\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFollow-up (n\u0026thinsp;=\u0026thinsp;214)\u003c/p\u003e\n \u003cp\u003e[n (%)] / (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLost to Follow-up (n\u0026thinsp;=\u0026thinsp;49)\u003c/p\u003e\n \u003cp\u003e[n (%)] / (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTest Statistic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56.91\u0026thinsp;\u0026plusmn;\u0026thinsp;12.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.67\u0026thinsp;\u0026plusmn;\u0026thinsp;14.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.086\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.279\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.520\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58(27.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7(14.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e156(72.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42(85.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducation level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.583\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.108\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJunior high school or below\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e131(61.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36(73.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh school or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83(38.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13(17.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarital status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.567\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.465\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnmarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13(6.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e196(91.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48(98.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDivorced/Widowed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5(2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmployment status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.636\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.727\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63(29.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12(24.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnemployed/Medical leave\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56(26.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15(30.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRetired\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95(44.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22(44.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRelationship to patient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.043\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.835\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpouse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e156(72.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35(71.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChild/Parent/Relative/Friend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58(27.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14(28.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDaily caregiving time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.717\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.190\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;8h\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e131(61.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25(51.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;8 h\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83(38.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24(49.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUse of electronic devices to learn information about stroke\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.250\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.617\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78(36.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16(32.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e136(63.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33(67.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eNote: a\u0026thinsp;=\u0026thinsp;Independent samples t-test; b\u0026thinsp;=\u0026thinsp;Chi-square test; c\u0026thinsp;=\u0026thinsp;Fisher\u0026rsquo;s exact test.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2. Dyadic self-care scores over time in stroke patients and caregivers\u003c/h2\u003e\n \u003cp\u003ePatient self-care maintenance scores showed an initial increase followed by a decline from T0 (66.92\u0026thinsp;\u0026plusmn;\u0026thinsp;15.02) to T3 (62.85\u0026thinsp;\u0026plusmn;\u0026thinsp;16.14). One-way repeated measures ANOVA indicated a statistically significant overall change (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Pairwise comparisons showed significant differences between all time points (T0 vs. T1, T0 vs. T2, T0 vs. T3, T1 vs. T2, T1 vs. T3, T2 vs. T3; all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Patient self-care monitoring scores declined from T0 (67.91\u0026thinsp;\u0026plusmn;\u0026thinsp;11.78) to T3 (57.18\u0026thinsp;\u0026plusmn;\u0026thinsp;12.79), with a statistically significant overall trend (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). All pairwise comparisons were statistically significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Patient self-care management scores increased from T0 (61.39\u0026thinsp;\u0026plusmn;\u0026thinsp;15.63) to T3 (69.86\u0026thinsp;\u0026plusmn;\u0026thinsp;17.35), with a significant overall trend (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Pairwise comparisons showed significant differences between all time points (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\n \u003cp\u003eCaregiver contributions to self-care maintenance decreased from T0 (69.31\u0026thinsp;\u0026plusmn;\u0026thinsp;14.33) to T3 (62.81\u0026thinsp;\u0026plusmn;\u0026thinsp;17.48). ANOVA indicated a significant overall change (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). No significant difference was found between T0 and T1 (P\u0026thinsp;=\u0026thinsp;0.170), while all other comparisons were statistically significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Caregiver contributions to self-care monitoring decreased from T0 (70.23\u0026thinsp;\u0026plusmn;\u0026thinsp;11.92) to T3 (59.52\u0026thinsp;\u0026plusmn;\u0026thinsp;13.93), with a significant overall trend (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). No significant difference was found between T0 and T1 (P\u0026thinsp;=\u0026thinsp;0.549), while other comparisons showed statistical significance (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Caregiver contributions to self-care management showed an initial increase followed by a decline, from T0 (65.23\u0026thinsp;\u0026plusmn;\u0026thinsp;16.34) to T3 (67.35\u0026thinsp;\u0026plusmn;\u0026thinsp;16.41), with a statistically significant overall trend (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). No significant differences were observed between T1 and T2 (P\u0026thinsp;=\u0026thinsp;0.746) or T1 and T3 (P\u0026thinsp;=\u0026thinsp;0.096), while other pairwise comparisons were significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). For details, see Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eScores of dyadic self-care in patients and caregivers at different time points (n\u0026thinsp;=\u0026thinsp;214 dyads)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eItem\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eT0\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePatient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP-Maintenance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e66.92\u0026thinsp;\u0026plusmn;\u0026thinsp;15.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e68.39\u0026thinsp;\u0026plusmn;\u0026thinsp;14.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e65.28\u0026thinsp;\u0026plusmn;\u0026thinsp;15.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e62.85\u0026thinsp;\u0026plusmn;\u0026thinsp;16.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44.491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP-Monitoring\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e67.91\u0026thinsp;\u0026plusmn;\u0026thinsp;11.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e66.24\u0026thinsp;\u0026plusmn;\u0026thinsp;12.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e62.07\u0026thinsp;\u0026plusmn;\u0026thinsp;13.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e57.18\u0026thinsp;\u0026plusmn;\u0026thinsp;12.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e96.271\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP-Management\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e61.39\u0026thinsp;\u0026plusmn;\u0026thinsp;15.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e64.50\u0026thinsp;\u0026plusmn;\u0026thinsp;15.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e67.55\u0026thinsp;\u0026plusmn;\u0026thinsp;16.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e69.86\u0026thinsp;\u0026plusmn;\u0026thinsp;17.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.943\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCaregiver\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC-Maintenance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e69.31\u0026thinsp;\u0026plusmn;\u0026thinsp;14.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e68.65\u0026thinsp;\u0026plusmn;\u0026thinsp;14.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e64.76\u0026thinsp;\u0026plusmn;\u0026thinsp;16.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e62.81\u0026thinsp;\u0026plusmn;\u0026thinsp;17.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC-Monitoring\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e70.23\u0026thinsp;\u0026plusmn;\u0026thinsp;11.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e68.13\u0026thinsp;\u0026plusmn;\u0026thinsp;12.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63.53\u0026thinsp;\u0026plusmn;\u0026thinsp;13.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e59.52\u0026thinsp;\u0026plusmn;\u0026thinsp;13.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e65.701\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC-Management\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e65.23\u0026thinsp;\u0026plusmn;\u0026thinsp;16.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e68.60\u0026thinsp;\u0026plusmn;\u0026thinsp;15.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e68.44\u0026thinsp;\u0026plusmn;\u0026thinsp;15.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e67.35\u0026thinsp;\u0026plusmn;\u0026thinsp;16.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.954\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eNote: P-Maintenance\u0026thinsp;=\u0026thinsp;patient self-care maintenance; P-Monitoring\u0026thinsp;=\u0026thinsp;patient self-care monitoring; P-Management\u0026thinsp;=\u0026thinsp;patient self-care management; C-Maintenance\u0026thinsp;=\u0026thinsp;caregiver contribution to self-care maintenance; C-Monitoring\u0026thinsp;=\u0026thinsp;caregiver contribution to self-care monitoring; C-Management\u0026thinsp;=\u0026thinsp;caregiver contribution to self-care management.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3. Group-based multi-trajectory analysis of dyadic self-care\u003c/h2\u003e\n \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\n \u003ch2\u003e3.3.1 Dyadic self-care maintenance\u003c/h2\u003e\n \u003cp\u003eUsing a group-based multi-trajectory model to analyze dyadic self-care maintenance scores in stroke patients and caregivers, model fit indices including AIC, BIC, SABIC, class probabilities, and AvePP values were evaluated. Models 5 and 6 were excluded due to class probabilities below 10% and suboptimal AvePP and entropy values. Among Models 1 to 4, the absolute values of AIC, BIC, and SABIC progressively decreased, reaching the lowest at the 4-class model, indicating the best fit. At this solution, the AvePP values for Groups 1 through 4 were 0.98, 0.96, 0.90, and 0.96, respectively. Detailed results are presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eGrouping criteria for dyadic self-care maintenance trajectory model\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber of Groups\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePolynomial\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAIC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBIC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSABIC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEntropy\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eClass Probability (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-7090.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-7107.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-7094.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 groups\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-6760.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-6792.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-6766.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.928\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55.30/44.70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 groups\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-6615.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-6662.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-6623.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.922\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.55/20.27/42.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 groups\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-6515.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-6577.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-6525.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.910\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.25/18.27/28.10/24.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 groups\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-6480.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-6558.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-6492.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.899\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.22/26.94/19.44/25.90/20.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 groups\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e333333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-6459.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-6552.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-6473.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.96/27.01/19.50/18.84/22.51/9.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eNote: AIC\u0026thinsp;=\u0026thinsp;Akaike Information Criterion; BIC\u0026thinsp;=\u0026thinsp;Bayesian Information Criterion; SABIC\u0026thinsp;=\u0026thinsp;Sample-size Adjusted BIC; Entropy\u0026thinsp;=\u0026thinsp;classification accuracy index.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eWhen all trajectories for dyadic self-care maintenance in stroke patients and caregivers were modeled using cubic polynomials (3333/3333), the trajectories of patients and caregivers in Groups 1 through 4 were not statistically significant. Dimensionality reduction was applied stepwise to the non-significant trajectories, followed by multiple iterations. The optimal polynomial specification was identified as 1110/1110. Under this model, the AIC was \u0026minus;\u0026thinsp;6512.28, BIC was \u0026minus;\u0026thinsp;6544.26, and SABIC was \u0026minus;\u0026thinsp;6491.87. The AvePP values for Groups 1 to 4 were 0.97, 0.92, 0.96, and 0.97, respectively, with an entropy value of 0.907. The class probabilities were 29.49%, 28.67%, 18.07%, and 23.77%, and all group trajectories were statistically significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating good model fit. See Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e for details.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eOptimal polynomial results for the dyadic self-care maintenance model\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOrder\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTerm\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEstimate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003ePatient\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGroup 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53.948\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.985\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.752\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLinear slope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.823\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.948\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGroup 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.311\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.292\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51.318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLinear slope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.434\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGroup 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78.101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLinear slope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.447\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGroup 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81.261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e89.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003eCaregiver\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGroup 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61.991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLinear slope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-7.138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGroup 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75.302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.611\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.750\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLinear slope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGroup 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56.335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.595\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLinear slope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.858\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGroup 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82.114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.808\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e101.687\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003eNote:Group 1\u0026thinsp;=\u0026thinsp;Dyadic Middle-Low Decrease Group; Group 2\u0026thinsp;=\u0026thinsp;Patient Middle-Low Decrease and Caregiver Middle-High Decrease Group; Group 3\u0026thinsp;=\u0026thinsp;Patient Middle-High Decrease and Caregiver Middle-Low Decrease Group; Group 4\u0026thinsp;=\u0026thinsp;Dyadic Middle-High Sustained Group\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eBased on prior findings, a self-care score above 70 indicates a high level of self-care in stroke patients, and a caregiver contribution score above 70 indicates a high level of caregiver involvement. Using this threshold to classify trajectory patterns, the four groups were named as follows: the \u0026quot;Dyadic Middle-Low Decrease Group\u0026quot; (29.49%), the \u0026quot;Patient Middle-Low Decrease and Caregiver Middle-High Decrease Group\u0026quot; (28.67%), the \u0026quot;Patient Middle-High Decrease and Caregiver Middle-Low Decrease Group\u0026quot; (18.07%), and the \u0026quot;Dyadic Middle-High Sustained Group\u0026quot; (23.77%). See Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\n \u003ch2\u003e3.3.2 Dyadic self-care monitoring\u003c/h2\u003e\n \u003cp\u003eBased on model fit indicators such as AIC, BIC, SABIC, category probabilities, and AvePP values, along with clinical relevance, the 4-group model was determined to be the optimal choice for dyadic self-care monitoring trajectory. At this point, the AvePP values for Group 1, Group 2, Group 3, and Group 4 were 0.95, 0.92, 0.92, and 0.95, respectively. The grouping criteria for the dyadic self-care monitoring trajectory model for stroke patients and caregivers are detailed in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eGrouping criteria for dyadic self-care monitoring trajectory model\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber of Groups\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePolynomial\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAIC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBIC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSABIC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEntropy\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eClass Probability (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 Group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6768.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6785.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6773.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 Groups\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6473.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6505.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6481.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.897\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.86/61.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 Groups\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6358.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6405.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6368.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.899\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.80/20.32/47.88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 Groups\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6255.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6317.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6269.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.890\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.10/31.29/18.97/24.63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 Groups\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6205.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6282.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6221.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.897\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.70/23.37/17.88/25.79/23.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 Groups\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e333333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6294.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6387.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6314.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.906\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.79/3.43/22.85/17.82/25.97/23.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eNote:AIC\u0026thinsp;=\u0026thinsp;Akaike Information Criterion; BIC\u0026thinsp;=\u0026thinsp;Bayesian Information Criterion; SABIC\u0026thinsp;=\u0026thinsp;Sample-size Adjusted BIC; Entropy\u0026thinsp;=\u0026thinsp;classification accuracy index.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eWhen the three-order terms (3333/3333) were applied to the dyadic self-care monitoring trajectories of stroke patients and caregivers, the statistical results for the trajectories in Groups 1, 2, 3, and 4 were not significant. After performing stepwise dimensionality reduction on the non-significant trajectories, multiple iterations were conducted to optimize the model. The final optimal polynomial was 2111/1111, with AIC = -6250.34, BIC = -6287.37, SABIC = -6238.62, and AvePP values of 0.95, 0.92, 0.92, and 0.95 for Groups 1, 2, 3, and 4, respectively. The Entropy index was 0.887, and the category probabilities for each group were 25.05%, 30.93%, 19.11%, and 24.91%, with all trajectories showing significant statistical significance (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating a good model fit, as shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab7\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eOptimal polynomial results for the dyadic self-care monitoring model\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOrder\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTerm\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEstimate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003ePatient\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGroup 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57.345\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.490\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.476\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLinear slope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.589\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.420\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSecond-order slope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGroup 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61.406\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLinear slope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.612\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGroup 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76.729\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61.257\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLinear slope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.727\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGroup 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79.593\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.945\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84.247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLinear slope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.797\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.835\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003eCaregiver\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGroup 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60.376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.533\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLinear slope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.279\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.811\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGroup 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76.375\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.453\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLinear slope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-7.126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGroup 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61.725\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.257\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49.107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLinear slope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.895\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGroup 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81.394\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75.918\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLinear slope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.714\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eNote:AIC\u0026thinsp;=\u0026thinsp;Akaike Information Criterion; BIC\u0026thinsp;=\u0026thinsp;Bayesian Information Criterion; SABIC\u0026thinsp;=\u0026thinsp;Sample-size Adjusted BIC; Entropy\u0026thinsp;=\u0026thinsp;classification accuracy index.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eBased on the distribution characteristics of each group, Group 1 was named the \u0026quot;Dyadic Middle-Low Decrease Group\u0026quot; (25.05%), Group 2 was named the \u0026quot;Patient Middle-Low Decrease and Caregiver Middle-High Decrease Group\u0026quot; (30.93%), Group 3 was named the \u0026quot;Patient Middle-High Decrease and Caregiver Middle-Low Decrease Group\u0026quot; (19.11%), and Group 4 was named the \u0026quot;Dyadic Middle-High Decrease Group\u0026quot; (24.91%). As shown in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\n \u003ch2\u003e3.3.3 Dyadic self-care management\u003c/h2\u003e\n \u003cp\u003eBased on model fit indicators such as AIC, BIC, SABIC, category probabilities, and AvePP values, as well as clinical significance, four groups were determined to be the optimal choice for the dyadic self-care management model. At this point, the AvePP values for Groups 1, 2, 3, and 4 were 0.99, 0.93, 0.97, and 0.97, respectively. The grouping criteria for the dyadic self-care management trajectory model are detailed in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab8\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eGrouping criteria for dyadic self-care management trajectory model\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber of Groups\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePolynomial\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAIC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBIC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSABIC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEntropy\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eClass Probability (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 Group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-7146.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-7163.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-7151.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 Groups\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6820.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6852.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6827.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.905\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.64/47.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 Groups\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6614.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6661.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6625.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.938\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.10/19.99/40.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 Groups\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6511.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6573.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6525.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.932\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.31/19.02/27.18/28.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 Groups\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6456.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6534.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6473.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.924\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.19/22.07/28.08/17.88/8.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 Groups\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e333333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6445.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6538.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6465.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.930\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.14/22.34/16.99/16.18/22.48/1.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eNote:AIC\u0026thinsp;=\u0026thinsp;Akaike Information Criterion; BIC\u0026thinsp;=\u0026thinsp;Bayesian Information Criterion; SABIC\u0026thinsp;=\u0026thinsp;Sample-size Adjusted BIC; Entropy\u0026thinsp;=\u0026thinsp;classification accuracy index.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eTo optimize the model, stepwise dimensionality reduction was applied to trajectories with non-significant statistical results, followed by multiple iterations. Ultimately, the best polynomial for the trajectories was 1121/1010. At this point, the model\u0026apos;s AIC was \u0026minus;\u0026thinsp;6507.66, BIC was \u0026minus;\u0026thinsp;6541.32, and SABIC was \u0026minus;\u0026thinsp;6492.57. The AvePP values for Groups 1, 2, 3, and 4 were 0.97, 0.97, 0.94, and 0.96, respectively. The Entropy index was 0.930, and the category probabilities for each group were 27.49%, 25.43%, 18.77%, and 28.31%. All groups had statistically significant trajectories (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating good model fit, as shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab9\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eOptimal polynomial results for the dyadic self-care management model\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOrder\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTerm\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEstimate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003ePatient\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGroup 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.821\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLinear slope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.362\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.290\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGroup 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57.575\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLinear slope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.695\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.440\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGroup 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63.162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLinear slope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.593\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.803\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.471\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSecond-order slope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGroup 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73.684\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLinear slope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.795\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.513\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003eCaregiver\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGroup 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.672\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.467\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLinear slope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.770\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.513\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGroup 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73.676\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.802\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91.912\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGroup 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53.505\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.616\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLinear slope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.627\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGroup 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82.827\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.753\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e109.996\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eNote:AIC\u0026thinsp;=\u0026thinsp;Akaike Information Criterion; BIC\u0026thinsp;=\u0026thinsp;Bayesian Information Criterion; SABIC\u0026thinsp;=\u0026thinsp;Sample-size Adjusted BIC; Entropy\u0026thinsp;=\u0026thinsp;classification accuracy index.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eBased on the distribution characteristics of each group, Group 1 is named the \u0026quot;Dyadic Middle-Low Increase Group\u0026quot; (27.49%), Group 2 is named the \u0026quot;Patient Middle-Low Increase and Caregiver Middle-High Sustained Group\u0026quot; (25.43%), Group 3 is named the \u0026quot;Patient Middle-High Increase and Caregiver Middle-Low Decrease Group\u0026quot; (18.77%), and Group 4 is named the \u0026quot;Patient Middle-High Increase and Caregiver Middle-High Sustained Group\u0026quot; (28.31%). Refer to Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4. Predictors of heterogeneous trajectories of dyadic self-care\u003c/h2\u003e\n \u003cp\u003eMultinomial logistic regression was performed with the dyadic self-care maintenance trajectory groups as the dependent variable. Predictors included patient and caregiver characteristics found significant in univariate analysis: patient education, ADL capability; caregiver gender, relationship to the patient, and daily caregiving time. Additional variables comprised disease knowledge, self-efficacy, mutuality, and perceived stroke environment for both parties. Group 4 (\u0026quot;Dyadic Middle-High Sustained Group\u0026quot;) served as the reference.Compared to Group 4, membership in Group 1 (\u0026quot;Dyadic Middle-Low Decrease Group\u0026quot;) was more likely when patients reported lower self-efficacy and poorer stroke environment, and caregivers were non-spousal, provided care\u0026thinsp;\u0026lt;\u0026thinsp;8 hours/day, and had lower self-efficacy (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Group 2 (\u0026quot;Patient Middle-Low Sustained and Caregiver Middle-High Sustained Group\u0026quot;) was associated with patients having low self-efficacy and poor mutuality, and caregivers providing\u0026thinsp;\u0026ge;\u0026thinsp;8 hours/day of care (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Group 3 (\u0026quot;Patient Middle-High Sustained and Caregiver Middle-Low Decrease Group\u0026quot;) was more likely when caregivers had limited stroke knowledge, lower self-efficacy, and caregiving time\u0026thinsp;\u0026lt;\u0026thinsp;8 hours/day (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\n \u003cp\u003eThe dyadic self-care monitoring trajectory served as the dependent variable in a separate multinomial logistic regression. Independent variables included statistically significant patient and caregiver factors from univariate analysis: patient education, employment status, electronic device usage for stroke learning, and ADL; caregiver employment and device usage. Also included were both parties\u0026apos; disease knowledge, self-efficacy, mutuality, and stroke environment. Group 4 was used as the reference. Patients not using electronic devices, with low knowledge, low self-efficacy, and negative perceptions of the stroke environment, were more likely to be in Group 1 (\u0026quot;Dyadic Middle-Low Decrease Group\u0026quot;) (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Group 2 (\u0026quot;Patient Middle-Low Decrease and Caregiver Middle-High Decrease Group\u0026quot;) was associated with low patient disease knowledge and self-efficacy (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Group 3 (\u0026quot;Patient Middle-High Decrease and Caregiver Middle-Low Decrease Group\u0026quot;) was linked to unemployed patients with high ADL scores and negative stroke environment, and caregivers who were also unemployed/on leave, with low self-efficacy and mutuality (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\n \u003cp\u003eFor the self-care management trajectory, a multinomial logistic model was constructed. Predictors included significant variables from univariate tests: patient education, ADL ability, use of electronic devices; caregiver-patient relationship, and device use. Additional variables included both parties\u0026rsquo; disease knowledge, self-efficacy, mutuality, and stroke environment. Group 4 served as the reference. Patients who did not use electronic devices, and had lower knowledge, self-efficacy, and perceived environmental support\u0026mdash;along with caregivers reporting low self-efficacy\u0026mdash;were more likely to belong to Group 1 (\u0026quot;Dyadic Middle-Low Increase Group\u0026quot;) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Group 2 (\u0026quot;Patient Middle-Low Increase and Caregiver Middle-High Sustained Group\u0026quot;) was associated with lower patient education, self-efficacy, mutuality, and stroke environment, and lower caregiver self-efficacy (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Group 3 (\u0026quot;Patient Middle-High Increase and Caregiver Middle-Low Decrease Group\u0026quot;) was more likely when patients did not use electronic devices, had good ADL function but poor environmental perception, and caregivers had lower self-efficacy (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Dyadic Self-Care Levels and Development Trends\u003c/h2\u003e\u003cp\u003eThe results of this study indicate that stroke patients and their caregivers demonstrate significant room for improvement in dyadic self-care behaviors\u0026mdash;including maintenance, monitoring, and management\u0026mdash;within the first year post-discharge (all scores\u0026thinsp;\u0026le;\u0026thinsp;70). These findings align with Locatelli et al., who reported similarly low levels of caregiver contribution to self-care in heart failure patients during a one-year follow-up [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. This highlights the persistent challenges in enhancing dyadic self-care in chronic disease contexts.\u003c/p\u003e\u003cp\u003eIn this study, patients\u0026rsquo; self-care maintenance and caregivers\u0026rsquo; contributions to maintenance behaviors showed an increasing trend from T0 to T1 (three months post-discharge), followed by a decline at T3. This differs from Pancani et al., whose study on 225 heart failure patients did not observe this pattern [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. One possible explanation is that daily health behaviors, health literacy, and treatment adherence were gradually neglected or forgotten by both patients and caregivers after the initial three-month period. Additionally, our findings suggest that caregivers\u0026rsquo; contributions to maintenance behaviors tend to diminish over time, indicating that targeted caregiver support or training may be particularly beneficial within the first three months post-discharge. Self-care monitoring behaviors by patients and caregivers exhibited a continuous decline from T0 (one month post-discharge) onward, suggesting low compliance in tracking symptoms and treatment responses, which deteriorated further over time. These findings underscore the urgent need for early interventions\u0026mdash;ideally within the first month post-discharge\u0026mdash;focused on improving self-care monitoring and ensuring ongoing evaluation to refine such interventions. In terms of management behaviors, patients demonstrated a rapid increase following discharge, which then stabilized. However, caregiver contributions declined at T2, indicating that as patients develop self-management capacity, caregivers may reduce their involvement. These dynamic patterns call for nuanced, time-sensitive interventions that address different phases of the dyadic self-care process. Recent longitudinal studies in other chronic conditions\u0026mdash;such as coronary heart disease and multimorbidity\u0026mdash;have also begun to explore dyadic self-care, which is crucial for the development of tailored theoretical models and intervention strategies in chronic disease management [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e4.2. Trajectory Groupings and Characteristics in Dyadic Self-Care Development\u003c/h2\u003e\u003cp\u003eGroup-based trajectory modeling revealed four distinct developmental trajectories for dyadic self-care in maintenance, monitoring, and management domains: (1) moderately low dyadic trajectory, (2) patient moderately low\u0026ndash;caregiver moderately high discordant trajectory, (3) patient moderately high\u0026ndash;caregiver moderately low discordant trajectory, and (4) moderately high dyadic trajectory. These results indicate that dyadic self-care behaviors evolve over time and exhibit substantial heterogeneity across the first year post-discharge. Future interventions should aim to identify these distinct patterns and implement stratified strategies accordingly.\u003c/p\u003e\u003cp\u003ePrior research has identified similar patterns in other populations. For instance, Kim et al. found three trajectories of self-care behaviors among heart failure patients: \"low-decreasing\" (20.9%), \"moderate-increasing\" (58.9%), and \"high-stable\" (20.2%) [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Son et al. identified \"high-stable\" and \"low-persistent\" groups in a one-year follow-up of 137 heart failure patients [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Dou et al. followed 119 patients with chronic obstructive pulmonary disease and reported three trajectories: \"continuously declining\", \"slightly increasing and stable\", and \"gradually improving\" [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. These findings differ from our study, possibly due to variations in disease type, follow-up duration, and patient characteristics. Nevertheless, they highlight the need for precision-based interventions targeting specific dyadic self-care trajectories in stroke populations.\u003c/p\u003e\u003cp\u003eThis study offers a novel perspective by recognizing the potential discordance in dyadic self-care development between patients and caregivers. For example, in the \u0026ldquo;patient moderately low\u0026ndash;caregiver moderately high\u0026rdquo; trajectory, patients\u0026rsquo; self-care remained low despite high caregiver engagement. This may reflect conflicts or misalignment in patient\u0026ndash;caregiver perspectives. Incorporating relational and communication-focused interventions into dyadic self-care programs may help close this gap and enhance outcomes for both parties [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e\u003cp\u003ePatients and caregivers classified in the \u0026ldquo;moderately low dyadic\u0026rdquo; group are at high risk for persistent self-care deficits and should be prioritized for more intensive and targeted interventions. In contrast, those in the \u0026ldquo;patient moderately high\u0026ndash;caregiver moderately low\u0026rdquo; or \u0026ldquo;moderately high dyadic\u0026rdquo; trajectories may require only minimal professional guidance. Self-help-based approaches, delivered through books, videos, websites, or digital tools, may support these dyads in independently strengthening their self-care capabilities [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e4.3. Factors Influencing Dyadic Self-Care Trajectories\u003c/h2\u003e\u003cp\u003eMultinomial logistic regression analysis identified several key factors influencing dyadic self-care trajectories. For patients, educational attainment, employment status, use of digital tools to learn about stroke, functional ability in daily living, disease-related knowledge, and self-efficacy were significant predictors. These findings suggest that future interventions should prioritize enhancing patients\u0026rsquo; functional independence, building digital literacy, and expanding access to digital health resources [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Moreover, training focused on disease knowledge and improving self-efficacy may facilitate more favorable self-care development trajectories [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eCaregiver-related factors included the relationship to the patient, employment status, hours spent caregiving daily, disease knowledge, and self-efficacy. De Maria et al. also found that non-spousal caregivers were more likely to fall into less favorable dyadic maintenance trajectories, supporting our findings [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. The process of becoming a well-informed, confident caregiver, termed caregiver activation, can positively influence dyadic self-care [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Future studies should integrate caregiver activation strategies into dyadic interventions, ensuring that caregivers are empowered to contribute meaningfully to patients\u0026rsquo; self-care.\u003c/p\u003e\u003cp\u003eThe interdependence between patients and caregivers plays a crucial role in shaping dyadic self-care trajectories. This aligns with dyadic illness management theory, which emphasizes the mutual dependence of patient\u0026ndash;caregiver dyads [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. For example, patients with lower interdependence were more likely to fall into the \u0026ldquo;patient moderately low\u0026ndash;caregiver moderately high\u0026rdquo; trajectory for self-care maintenance and management. In contrast, low caregiver interdependence was associated with the \u0026ldquo;patient moderately high\u0026ndash;caregiver moderately low\u0026rdquo; trajectory in self-care monitoring. These patterns indicate that enhancing relational interdependence may steer dyads toward more optimal self-care outcomes. Therefore, interventions should include components that foster emotional closeness and collaborative partnership between patients and caregivers [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFinally, the patient's perception of environmental support significantly influenced the trajectories of self-care monitoring and management. Patients in more supportive environments were more likely to engage actively in managing their condition, supporting findings from prior qualitative reviews [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. This highlights the importance of identifying and improving environmental factors, such as home and community infrastructure, that may otherwise hinder dyadic self-care development [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e4.4. Implications for practice and research\u003c/h2\u003e\u003cp\u003eThis study highlights the heterogeneous and dynamic nature of dyadic self-care behaviors among stroke patients and their caregivers within the first year post-discharge. Early and continuous support, particularly within the first three months, is crucial. In clinical practice, targeted and stratified interventions should be developed based on distinct dyadic self-care trajectories (e.g., low-low, discordant types). Enhancing the quality of the dyadic relationship is essential to foster collaboration between patients and caregivers. Environmental support should also be optimized through community and home-based resources. Future research should further explore the mechanisms underlying dyadic self-care changes and develop personalized, technology-assisted interventions to improve accessibility and effectiveness.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\u003ch2\u003e4.5. Limitations\u003c/h2\u003e\u003cp\u003eThis study has several limitations that should be considered when interpreting the findings. Firstly, due to constraints in manpower, resources, and time, the sample was limited to 263 stroke patient-caregiver dyads, with 49 dyads lost to follow-up. The relatively small sample size and high attrition rate may limit the generalizability of the results and introduce potential bias. Secondly, the early stages of the study were conducted during the COVID-19 pandemic. Government-imposed restrictions and lockdown measures may have influenced the actual self-care behaviors of patients and caregivers, potentially affecting the ecological validity of the findings. Researchers should exercise caution when comparing these results to those of studies conducted under different public health conditions. Thirdly, patients with cognitive impairments were excluded from participation, which may restrict the applicability of the findings to the broader stroke population. Future research should consider including individuals with varying levels of cognitive function to develop comprehensive interventions that reflect the heterogeneity of stroke survivors.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThe developmental trajectory of dyadic self-care among stroke patients and their caregivers demonstrates significant heterogeneity and time-dependent variability across self-care maintenance, monitoring, and management behaviors. These findings highlight the importance of adopting a longitudinal and dyadic perspective in both clinical practice and research. Future studies should aim to identify distinct dyadic self-care trajectory subgroups and explore the key influencing factors, including individual characteristics, relationship dynamics, and environmental conditions. By enhancing digital literacy, strengthening the patient-caregiver relationship, and improving the care environment, it is possible to support more favorable dyadic self-care outcomes over time.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eDALYs: disability-adjusted life years. COPD: chronic obstructive pulmonary disease. MBI: Modified Barthel Index. SCSI: the Self-Care of Stroke Inventory. CCSCSI: the Caregiver Contributions to Self-Care of Stroke Inventory. SCSES: the Self-Care Self-Efficacy Scale. CSE-CSC: the Caregiver Self-Efficacy in Contributing to Patient Self-Care Scale. MS: the Mutuality Scale. MOSE: the Measure of Stroke Environment. GBmTM: Group-Based Multi-Trajectory Modeling. AIC: Akaike Information Criterion. BIC: Bayesian Information Criterion. SaBIC: Sample-Size Adjusted BIC. Entropy: a measure of classification accuracy. AvePP: Average Posterior Probability.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Research Ethics Committee of Zhengzhou University (ZZUIRB2021-115). All participants provided informed consent prior to participation. The study was conducted in accordance with the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData is provided within the manuscript or supplementary information files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo conflict of interest has been declared by the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Competing Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the National Natural Science Foundation of China [Grant No. 72174184], the Youth Science Fund of the National Natural Science Foundation of China [Grant No. 72004205], and the Henan Provincial Science and Technology Innovation Talent Program [Grant No. 134200510018].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWenna Wang: Writing – original draft, visualization. Beilei Lin: Methodology, formal analysis. Pucciarelli Gianluca: Writing – review and editing. Qiushi Zhang: Data curation, investigation. Jing Cao: Methodology, Writing – review and editing. Yongxia Mei: Conceptualization, formal analysis. Zhenxiang Zhang: Conceptualization, supervision.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors extend their appreciation for the contributions of all stroke patients, caregivers, and other relevant individuals who participated in the questionnaire survey.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGBD 2021 Stroke Risk Factor Collaborators. Global, regional, and national burden of stroke and its risk factors, 1990\u0026ndash;2021: a systematic analysis for the Global Burden of Disease Study 2021. 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J Stroke Cerebrovasc Dis. 2022;31:106305. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jstrokecerebrovasdis.2022.106305\u003c/span\u003e\u003cspan address=\"10.1016/j.jstrokecerebrovasdis.2022.106305\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-nursing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurs","sideBox":"Learn more about [BMC Nursing](http://bmcnurs.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurs/default.aspx","title":"BMC Nursing","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Stroke, Caregiver, Dyads, self-care, Group-based multi-trajectory modelling, Determinants","lastPublishedDoi":"10.21203/rs.3.rs-7060592/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7060592/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003ePromoting self-care is widely recommended as an effective approach to reducing the burden of stroke. As a dyadic process, it involves the active participation of both patients and caregivers. However, its complexity and long-term nature are still not well understood.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective: \u003c/strong\u003eTo investigate the developmental trajectories of dyadic self-care in stroke patients and caregivers, elucidate distinct trajectory patterns and their influencing factors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDesign: \u003c/strong\u003eA longitudinal multi-center study was conducted.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSettings:\u003c/strong\u003e Outpatient and community settings in China.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e A total of 214 stroke patient–caregiver dyads completed data collection at four time points: 1 month (T0), 3 months (T1), 6 months (T2), and 12 months (T3) following discharge after a first-ever stroke. Group-based multi-trajectory modeling was employed to identify the heterogeneity of the trajectories of the dyadic self-care maintenance, monitoring, and management among stroke patients and caregivers. Multiple logistic regression was used to explore the predictors of heterogeneous trajectories of dyadic self-care.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eFour distinct trajectories of dyadic self-care maintenance were identified in stroke dyads: \"Dyadic Middle-Low Decrease\" (29.49%), \"Patient Middle-Low Decrease and Caregiver Middle-High Decrease\" (28.67%), \"Patient Middle-High Decrease and Caregiver Middle-Low Decrease\" (18.07%), and \"Dyadic Middle-High Sustained\" (23.77%). For dyadic self-care monitoring, the trajectories included: \"Dyadic Middle-Low Decrease\" (25.05%), \"Patient Middle-Low Decrease and Caregiver Middle-High Decrease\" (30.93%), \"Patient Middle-High Decrease and Caregiver Middle-Low Decrease\" (19.11%), and \"Dyadic Middle-High Decrease\" (24.91%). Dyadic self-care management trajectories comprised: \"Dyadic Middle-Low Increase\" (27.49%), \"Patient Middle-Low Increase and Caregiver Middle-High Sustained\" (25.43%), \"Patient Middle-High Increase and Caregiver Middle-Low Decrease\" (18.77%), and \"Patient Middle-High Increase and Caregiver Middle-High Sustained\" (28.31%). Multiple logistic regression analysis identified several significant predictors of dyadic self-care trajectories, including patients’ self-efficacy, mutuality, knowledge, stroke environment, use of electronic devices, employment and education status, as well as caregivers’ self-efficacy, mutuality, caregiving hours, relationship with the patient, knowledge, and employment status.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions and Implications: \u003c/strong\u003eThe developmental trajectory of dyadic self-care for stroke patients and caregivers exhibits heterogeneity, suggesting that future research should integrate the longitudinal changes in dyadic self-care characteristics of patients and caregivers and trajectory classification, focusing on its influencing factors for precise classification and intervention.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRegistration:\u003c/strong\u003e Clinical trial number: not applicable.\u003c/p\u003e","manuscriptTitle":"Longitudinal study of Dyadic Self-Care in Stroke Patients and Caregivers: A Group-Based Multi-Trajectory Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-16 01:57:22","doi":"10.21203/rs.3.rs-7060592/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"216881750432891215905377269950137810025","date":"2025-11-09T18:10:12+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-31T10:39:07+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-28T02:27:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"29778540356479888767314104757277425014","date":"2025-10-23T13:05:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"17695413160204905808958962131148092681","date":"2025-10-21T23:17:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"149287719637363927270666207852985057409","date":"2025-10-13T03:37:49+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-02T09:42:37+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-18T11:20:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-15T20:01:54+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-15T15:48:40+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Nursing","date":"2025-07-15T14:43:14+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-nursing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurs","sideBox":"Learn more about [BMC Nursing](http://bmcnurs.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurs/default.aspx","title":"BMC Nursing","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1e37f19c-75cf-4c22-855c-f98be61f4918","owner":[],"postedDate":"October 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-10-16T01:57:23+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-16 01:57:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7060592","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7060592","identity":"rs-7060592","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}