Proactive Health Profiling and Classification of Older Adults with Multimorbidity in Rural China: A Qualitative Study

Abstract

French

Our study aimed to identify and classify proactive health management patterns among rural older adults with multimorbidity using a user persona approach. We conducted a descriptive qualitative study with user persona methodology in a rural area of South China from February to May 2025, where 33 rural adults aged ≥60 years with ≥2 chronic conditions were recruited via purposive sampling. We used semi-structured interviews to explore key dimensions of proactive health management, including health cognition, resource use, and information acquisition; we coded and analyzed interview data using NVivo 12 to extract behavioral characteristics and construct persona categories. Our analysis of the interview data from 33 participants identified 5 typical user personas: tradition-oriented self-management, low perceived risk management, family-delegated management, resource-constrained striving, and actively learning management. The findings indicate that rural older adults with multimorbidity exhibit diverse proactive health management patterns shaped by cultural beliefs, family dynamics, and resource constraints. Recognizing this heterogeneity is essential for moving beyond uniform health promotion approaches and supporting more responsive chronic disease management.

Keywords

multimorbidity rural older adults proactive health management user personas qualitative research China 多病共存农村老年人主动健康管理用户画像定性研究中国

Introduction

With global population aging, the prevalence of chronic non-communicable diseases (NCDs) continues to rise (World Health Organization (WHO), 2021). Multimorbidity, defined as the coexistence of two or more chronic conditions in an individual, has become a major global public health concern (Zhao et al., 2020). Approximately one-third of adults worldwide are affected, with the issue particularly pronounced among older adults and in low-income countries (Chowdhury et al., 2023). As the world’s most populous developing country, China is also facing substantial health challenges associated with multimorbidity, especially in rural areas.

To address the growing public health burden of multimorbidity, healthcare systems worldwide are shifting from passive, treatment-centered paradigms to proactive, patient-centered chronic disease management models (Waldman & Terzic, 2019; Yue, 2025). Proactive health management emphasizes enhancing individuals’ control over their health through improved health cognition, engagement in positive behaviors, and access to relevant resources (Koikai & Khan, 2023). Evidence from diverse settings indicates that such approaches can optimize care coordination, increase service uptake, and improve intermediate health outcomes such as blood pressure and blood glucose control. For example, in the United Kingdom, the National Health Service (NHS) has pioneered proactive care models, including the Proactive Care Framework for frailty (Hendry & Law, 2024) and the Enhanced Health in Care Homes (EHCH) program (NHS England, 2023). Centered on multidisciplinary care, risk stratification, and self-management support, these initiatives have significantly improved care experiences and continuity among older adults with complex needs (Hendry & Law, 2024). In China, the concept of “proactive health management” has been formally incorporated into national health agendas under Healthy China 2030 and related policy documents (Ministry of Science and Technology of the People’s Republic of China, 2017). Implementation efforts, such as family doctor contracting services (Hendry & Law, 2024) and chronic disease management programs (Hui et al., 2024), have shown early progress in improving risk factor control, monitoring, and individual engagement.

However, despite substantial national investment in chronic disease management at both medical and local levels, current proactive health management remains suboptimal. Prior studies have shown that older adults with chronic diseases in rural areas often misinterpret “proactive health management” as passive treatment-seeking, leading to insufficient self-initiated behavior (Huang et al., 2025; Tang et al., 2024). Only 37.4% of rural elderly undergo annual physical examinations, and fewer than 30% of those with hypertension or diabetes receive standardized treatment (Wu & Liu, 2020). Existing studies predominantly focus on demographic or clinical indicators (e.g., disease type or number) to predict health outcomes, while often neglecting other critical dimensions of heterogeneity, including health beliefs (Tian et al., 2024), self-management ability (Zhong et al., 2023), functional status (Marengoni et al., 2011), behavioral motivation (Coventry et al., 2015), and life context (Chowdhury et al., 2023). As a result of insufficient fine-grained classification and neglect of substantial within-group heterogeneity, strategies and services are commonly implemented using a “one-size-fits-all” approach, which limits relevance and adaptability (He et al., 2022). Under such uniform management models, resources are distributed broadly and evenly, but fail to effectively reach individuals with the most urgent needs and specific support requirements, eventually resulting in a dilemma of high investment, low effectiveness, and low efficiency (Sun et al., 2024). Several international studies have identified inadequate understanding of target populations’ behavioral characteristics and needs as a key bottleneck restricting the implementation of proactive health management services (J. Liu et al., 2022).

To bridge this research gap, “user personas” have emerged as an important tool for identifying target population needs and enhancing service adaptability. The core concept is to generate representative population roles through data collection, attribute extraction, and model construction, thereby identifying heterogeneous behavioral patterns and key needs (Qiu et al., 2026). Accordingly, this study adopts a descriptive qualitative research design, combined with a user personas approach, to examine the experiences of older adults living in a rural region in South China. By identifying behavioral characteristics and barriers to proactive health management among older adults with multimorbidity, this study aims to construct representative persona types to support stratified interventions and targeted resource allocation, and to promote the effective implementation of proactive health management among rural elderly populations.

Method

This study adopted a descriptive qualitative design (Sandelowski, 2010), aiming to explore in depth the lived experiences, barriers, and support needs of older adults with multimorbidity in rural China during the process of proactive health management. This approach aligns with our objective of providing a comprehensive and detailed account of the phenomenon, and facilitates the capture of diverse needs and authentic voices of older adults (Sandelowski, 2010).

Study Setting

The fieldwork was conducted between February and May 2025 in an underdeveloped rural area in Guangdong Province, China. The study site was characterized by relative shortages of health resources and a higher proportion of older residents. Adults aged ≥60 years accounted for more than 12% of the local population, and the density of licensed physicians (1.67 per 1,000 residents) was notably below the provincial average.

Participants

Participants were selected using purposive sampling based on the principle of maximum variation in order to ensure heterogeneity (in terms of gender, disease duration, economic conditions, and living arrangements).

Inclusion criteria: (1) Age ≥60 years; (2) Diagnosis of ≥2 chronic conditions consistent with the World Health Organization’s definition of non-communicable diseases, including hypertension, diabetes, heart disease, stroke, chronic pulmonary diseases (e.g., chronic obstructive pulmonary disease, asthma), osteoarthritis, cancer, memory-related conditions (e.g., Alzheimer’s disease), and other chronic diseases involving the kidney, stomach, eye, or liver, covering 12 common categories of chronic illness. Diagnosis was verified through medical records or self-report confirmed by community physicians (World Health Organization (WHO), 2021); (3) Permanent residence in a rural area; (4) Willingness to participate in interviews and allow audio recording.

Exclusion criteria included: (1) the presence of severe complications or organic diseases (e.g., severe organ failure such as advanced heart failure, end-stage renal failure, or advanced malignant tumors with extensive metastasis accompanied by cachexia); and (2) severe communication impairments or significant cognitive or psychiatric disorders.

Recruitment

The research team collaborated with local general practitioners (GPs), who assisted in identifying eligible participants. As liaisons within the community health service system, GPs assisted the research team solely in identifying and recommending potential participants who met the inclusion criteria; they were not involved in the final screening of the sample or in recruitment decisions. For potential interviewees, researchers first made initial contact by telephone and independently introduced the study purpose, interview content, and modes of participation. Home interviews were scheduled only after explicit consent was obtained from the participant. The interview location was chosen by the participant, typically a relatively quiet and private space within their residence. Interviews were primarily conducted by two researchers (HBL and YRH) who had received formal training in qualitative research methods, using a semi-structured interview approach. In cases where a GP was present during part of the interview, the GP remained solely as an observer and did not participate in questioning or interaction. Prior to the commencement of the interview, researchers privately reconfirmed the participant’s willingness for the GP to be present and explicitly informed them of their right to request the GP to leave at any time. If the participant expressed any discomfort or hesitation, the GP was asked to leave immediately.

Research Team

The principal investigator is a female associate professor of nursing who holds a PhD in Nursing, with over 20 years of experience in chronic disease management and qualitative research, and has long been engaged in health promotion among older adults in both urban and rural settings. The research team also included six master’s students and two undergraduate students in nursing, who had received systematic training in qualitative research methods, as well as three community physicians from the study area who were familiar with the local cultural context and linguistic characteristics.

Data Collection

Upon arrival at interview sites, researchers introduced the study and reiterated principles of confidentiality and privacy protection. Written informed consent was obtained from all participants; oral consent was audio recorded for illiterate individuals. An initial interview guide was developed based on literature review and expert consultation. Pilot testing with three older adults revealed that the term “proactive health management” was not commonly used by rural elders. Therefore, after obtaining consent, interviewers provided a brief, plain-language explanation in the local dialect (e.g., “By ‘proactive health management,’ we mean taking initiative in daily life to maintain health, such as routinely checking blood pressure, taking medications as prescribed, seeking health information, or participating in community health activities.”). Following refinement, the final interview guide included: (1) Basic information and disease-related conditions; (2) Awareness, perceptions, and attitudes toward proactive health management behaviors; (3) Actual engagement in proactive health management practices and related outcomes; (4) Facilitators and barriers to behavior engagement; (5) Needs for support and resources during proactive health management. One-on-one semi-structured in-depth interviews were used to elicit participants’ views and experiences. Interviews lasted an average of 47 min (range 38–61 min), were audio-recorded verbatim, and transcribed.

The number of interviews was determined based on considerations of data sufficiency. Data collection was conducted in an iterative manner. As data analysis progressed, the research team continuously assessed whether newly conducted interviews introduced additional proactive health management-related characteristics, analytical dimensions, or configurations of persona attributes. After interviewing the 31st participant, no new analytical dimensions related to proactive health management practices were identified. Moreover, the configurations of persona attributes within the existing dimensions had become stable, showing only repetition or further refinement of previously identified patterns. To verify this assessment, two additional older adults (the 32nd and 33rd participants) were interviewed. After confirming that no new information arose, the research team reached a consensus that the collected data adequately covered the range of experiences necessary to achieve the study objectives, and data collection was subsequently terminated.

Of the 36 older adults invited, 33 (83.33%) agreed to participate. Reasons for nonparticipation included lack of time (n = 1), deteriorating health (n = 1), and relocation (n = 1). No repeat interviews were conducted.

Data Analysis

Extraction of Role-Label Dimensions for Proactive Health Management Among Older Adults With Multimorbidity

This study employed an inductive qualitative content analysis approach to systematically analyze the interview data (Elo & Kyngäs, 2008). The analysis was conducted jointly by two researchers (HLM and YXH), with NVivo 12 Plus software used for data management and analysis. This method emphasizes that, without imposing a preconceived classification framework, analytical dimensions are constructed gradually from participants’ authentic narratives, to reflect the diversity and complexity of proactive health management practices among rural older adults with multimorbidity (Lyhne et al., 2025).

Considering that some interviews were conducted in the local dialect, all interviews were first transcribed verbatim in the source language (Dialect/Mandarin), retaining expressions embedded in their cultural context. Subsequently, bilingual researchers with backgrounds in medicine and social sciences and familiarity with the local linguistic and cultural context translated the texts into English. The translation followed the principle of “semantic equivalence.” For dialect expressions that were difficult to translate literally, the research team engaged in discussion and added annotations to preserve the original semantic connotations. When necessary, the original audio recordings were revisited for semantic verification to reduce potential meaning shift during the translation process (van Nes et al., 2010).

The interview transcripts were then imported into NVivo 12 Plus to establish a textual database, and open coding was conducted. HLM and YXH independently and repeatedly read all interview transcripts to gain an overall understanding of the proactive health management experiences of rural Chinese older adults with multimorbidity in relation to disease management, health behavior choices, and resource utilization. Subsequently, the two researchers conducted line-by-line coding of the texts, extracting key statements relevant to the research objectives and generating initial coding units.

On this basis, through constant comparison and progressive abstraction, the research team integrated initial codes that were semantically similar and reflected comparable proactive health management orientations into higher-level analytical expressions. Expressions with similar meanings were simplified and refined into standardized phrases characterized by conciseness and conceptual abstraction.

These phrases were used to summarize proactive health management characteristics that recurred across multiple interviews and held interpretive significance, and they further developed into several role-label dimensions. Coding discrepancies were resolved through team discussion until consensus was reached. All dimensions were required to be traceable to specific interview excerpts to ensure the auditability of the analytical process.

Construction of Proactive Health Management Personas Based on Cross-Dimensional Feature Integration

Following the extraction of role-label dimensions, the study proceeded to the stage of user persona construction. User personas were not determined by any single dimension in isolation; rather, they were generated through analytic clustering based on the configurational combinations of multidimensional characteristics at the individual level (Husain et al., 2024).

Individual Feature Extraction and Development of Multidimensional Individual Profiles

Building upon the predefined role-label dimensions, HLM and YXH conducted case-by-case analyses of the 33 participants. The researchers systematically synthesized each participant’s performance across all dimensions, thereby producing a multidimensional, individual-level profile for every case. Subsequently, preliminary grouping of cases was conducted according to the following principles:

(1) Criteria for Similarity Determination: Individuals exhibit similar behavioral orientations, decision-making logic, and resource utilization patterns across multiple core dimensions, rather than showing similarity in only a single dimension. (2) Holistic Configuration Priority Principle: Grouping is based on the overall consistency of characteristic combinations, rather than the cumulative quantity of individual traits. (3) Handling of Boundary Cases: For individuals displaying transitional characteristics between different types, the research team determines their classification by repeatedly revisiting the original data and relying on their dominant practice patterns, rather than forcibly dividing them or assigning them to multiple categories.

During this process, three additional researchers independently reviewed the individual feature descriptions and the preliminary grouping results. Consensus was achieved through collective discussion to enhance the analytic consistency of type construction.

Participant Validation and Feature Calibration

The research team then returned the preliminary multidimensional individual profiles to the corresponding participants for validation, inviting their interpretations and supplementary comments. This step was primarily intended to verify whether the researchers’ interpretations of the interview data were consistent with participants’ lived experiences, and to correct any potential misunderstandings.

Cross-Dimensional Comparison and Similarity-Based Classification

After completing individual grouping and calibration, the research team conducted cross-dimensional comparisons across all cases and further integrated characteristic configurations. Ultimately, five representative types of proactive health management user personas were identified. Each persona reflects a distinct configuration of characteristics at the analytical level. While clear conceptual distinctions were maintained between personas, it is acknowledged that individuals in real-world settings may not fit exclusively into a single category.

To enhance the clarity and interpretability of the personas, their descriptions were integrated into the data analysis process and presented using a combination of visual elements and textual narratives. Each persona was depicted as a fictitious character profile, accompanied by descriptive labels summarizing basic characteristics (e.g., age, disease duration) and core features of proactive health management (e.g., health behaviors and cognitive level). Representative quotations were selected according to the following criteria: (1) coverage of variability in age, gender, socioeconomic characteristics, and comorbidity; (2) nomination by three researchers as best reflecting the behavioral and cognitive features of each persona; and (3) consistency with similar expressions reported by at least two different participants. The research team jointly reviewed and confirmed the persona profiles and quotations to ensure credibility and appropriateness.

Rigor

Rigor was established by evaluating the credibility, confirmability, dependability, and transferability of the data (Johnson et al., 2020). To enhance credibility and safeguard voluntary participation and the security of information disclosure, participants were explicitly informed prior to the interviews that their participation was entirely voluntary: they could decline to answer any question or withdraw from the interview at any time without any consequences for their current or future medical care. This procedure was intended to minimize potential social desirability bias to the greatest extent possible.

Given that general practitioners (GPs) were present during some interviews, the research team further clarified that the presence of a GP was not a requirement of the study. Participants were free to decide whether to permit the GP’s presence without any adverse consequences. It was also clearly stated that the interview content would be used solely for research purposes, would not be incorporated into medical records, and would not be disclosed or fed back to the GP or any other healthcare professionals in any form.

All interviews were audio-recorded in their entirety after obtaining written informed consent. Only the participant, the researcher, and (if consented to) the GP were present during the interview sessions to ensure privacy. To ensure confirmability and confidentiality, audio recordings and verbatim transcripts were stored on encrypted, password-protected research devices accessible only to core members of the research team. All transcripts were anonymized prior to analysis: names, specific geographic locations, and other potentially identifiable information were removed or replaced, and each participant was assigned a unique study identification number.

Throughout the research process, team members engaged in reflexive practice prior to data collection, critically examining how their professional backgrounds and social identities might influence interview interactions and data interpretation. Reflexive journals were maintained continuously during the study to support confirmability and dependability. By providing detailed descriptions of the research context, participant characteristics, recruitment procedures, and data collection and analytic processes, this study offers sufficient information for readers to assess the applicability of the findings to other similar primary rural community settings, thereby enhancing transferability. The research team further reflected, in the limitations section, on the potential influence of existing clinical relationships within primary community settings on participants’ information disclosure.

Ethical Considerations

This study was conducted in accordance with the Declaration of Helsinki and was approved by the Institutional Review Board of the School of Medicine, Jinan University (No. JNUKY-2024-0045). All participants provided written informed consent after being informed of the study purpose, procedures, voluntary nature of participation, and their right to withdraw at any time. Participant information and interview data were anonymized to ensure confidentiality.

Results

Characteristics of Participants

A total of 33 older adults with multimorbidity living in rural areas were enrolled in this study, including 16 males and 17 females. Their ages ranged from 60 to 91 years, with a median age of 76 years. Participants with junior high school education or above accounted for 30.3%, and 63.6% were living with their spouse. The vast majority (87.9%) of participants had two chronic conditions, while 12.1% had three or more chronic conditions. See Table 1 for more details of the participants.

Table 1.

Demographic Characteristics of Rural Older Adults with Multimorbidity.

Demographic characteristics	Median (M)	Interquartile range (P25, P75)	Number (N)	Proportion (%)
Age	76	65, 84
Sex
Male			16	48.5
Female			17	51.5
Educational level
Primary school or below			23	69.7
Junior high school			8	24.2
Senior high school or above			2	6.1
Number of chronic conditions
2 conditions			29	87.9
3 or more conditions			4	12.1
Disease duration (years)	10	5, 16
Living arrangement
Living alone			4	12.2
Living with spouse			21	63.6
Living with children			8	24.2
Employment status
Yes			20	60.6
No			13	39.4
Main source of income
Wage income			11	33.3
Financial support from children			16	48.5
Pension			6	18.2

User Personas of Proactive Health Management Among Rural Older Adults With Multimorbidity

Five user personas for proactive health management were constructed: traditional-oriented self-management, low perceived risk management, family-delegated management, resource-constrained striving, and actively learning management. Each of these personas is discussed in the following sections, with key features summarized in Table 2 to facilitate comparison.

Table 2.

Classification of User Personas for Proactive Health Management Among Rural Older Adults with Multimorbidity.

Name	Tradition-oriented self-management	Low perceived risk management	Family-delegated management	Resource-constrained striving	Actively learning management
Representative participants	P1, P2, P4, P8, P10, P13, P16, P24, P28, P26	P5, P23, P14, P15, P21	P3, P9, P17, P22, P27, P30, P31	P6, P7, P11, P12, P18, P19, P25, P29, P32	P13, P20, P33
Age (years)	91–65(76.4)	87–60(72.2)	84–63(71.7)	87–62(76.7)	80–60(68.7)
Sex	5 male/5 female	4 male/1 female	3 male/4 female	2 male/7 female	2 male/1 female
Typical characteristics	Perceives health as the absence of pain or impairment in labor capacity, based on personal experience and long-established traditional medical beliefs	Views multiple chronic conditions as a natural part of aging, and lacks a sense of urgency regarding long-term risks.	Perceives health as a family matter, with the individual’s health role being weakened.	Has the willingness to actively improve health, but repeatedly faces setbacks due to multimorbidity and resource constraints.	Understands multimorbidity risks, actively accesses health information, and demonstrates proactive management awareness.
Decision-making patterns	• Makes independent judgments based on personal experience.• Little proactive consultation.	• Passive-reactive decision-making.• Action is only taken when symptoms worsen.	• Decision-making power is held by children or spouses.	• Decision-making is led by the individual, but implementation is highly restricted.	• The individual leads decision-making and proactively interacts with medical staff.
Health behavior patterns	• Relies on personal experience and traditional local remedies for disease management.	• Focuses on the most prominent symptoms while neglecting other minor symptoms.	• Medication, follow-up visits, and insurance affairs are handled by children or spouse.	• Pays attention to multiple diseases but struggles to balance their management.	• Health behavior patterns
Resource accessibility	• Certain medical resources are accessible.• Low trust in the modern healthcare, leading to hesitant service use.	• Overall accessibility of medical resources.• Trusts in the modern healthcare system, but lack of motivation for proactive utilization.	• Resource access is influenced by family factors• Prone to delays in seeking care due to children’s migrant work.	• Weak resource accessibility.• Faces multiple barriers (transport, economy, mobility).	• Strong ability to utilize medical resources.• Accesses insurance, village clinics, and family doctors.
Health information behaviors	• Relies on orally transmitted traditional health knowledge and rarely seeks modern medical information.	• Passively receives health information but shows limited engagement in applying it.	• Receives health information indirectly through family members with minimal personal engagement.	• Attempts to access health information but experiences fragmented acquisition and difficulty forming a coherent understanding.	• Actively seeks and integrates health information through multiple channels (e.g., television, village doctors).

Persona 1: Tradition-Oriented Self-Management

Definition: These older adults consider “being able to eat and work” as the core standard of health, equating health with the absence of pain or functional impairment. Their conception of health is rooted in life experience. They usually rely on traditional therapies for disease management

Portrait Characteristics:

Functional maintenance as the primary health criterion: They define health purely as being able to eat and perform daily activities, lack awareness of early disease screening and intervention, and fail to recognize that chronic diseases are often insidious and progressive even without obvious symptoms.

Caution and selective acceptance of modern medical care: They hold misunderstandings and wariness toward physical examinations, medication, and hospital-based tests, showing distrust in modern medical technologies and institutions.

Preference for traditional experiential judgment: Health assessments and disease responses rely heavily on traditional experience and subjective feelings. They lack scientific knowledge of chronic diseases and standardized management behaviors, resulting in poor continuity in medication use and follow-up visits.

Health Service Needs:

Lifestyle-oriented health education: Health information should start from daily life and functional maintenance, and explain the necessity of chronic disease management in popular language combined with traditional experience.

Trustworthy messengers as key communicators: They are more likely to accept health advice from opinion leaders such as village doctors, family members, or village cadres, who play a critical role in information transmission and behavior guidance.

Interventions embedded in daily community contexts: Health management interventions should be delivered through daily scenarios such as group activities and village health stations, rather than relying on individuals to actively seek formal medical care.

Representative Quotes:

Patient 1: “Proactive health management is very simple. For someone like me, as long as I can eat well every meal, stay warm, and don’t get seriously ill, being able to eat and work means I’m being active about my health.”

Patient 10: “I had a physical check-up a few years ago, and then I stopped going. They draw so much blood that it makes you weak, and then you have to spend money on supplements afterward—not worth it. At my age, not checking is fine. As long as I’m alive, it’s good enough.”

Patient 16: “When you’re old, just let nature take its course. Health doesn’t need to be taken too seriously. We survived by working and eating all those years; our ancestors were the same. No need to think about learning this or that every day.”

Persona 2: Low Perceived Risk Management

Definition: This group of older adults generally views multiple chronic conditions as common phenomena accompanying aging. When daily life is not significantly affected, they tend to perceive their health status as “fair” and lack a sense of urgency regarding chronic disease-related risks. They only adjust their behaviors when changes in physical conditions occur.

Portrait Characteristics:

Lack of continuity in health behaviors: Chronic disease management is fragmented and reactive. Participants take action only when significant discomfort occurs, lacking awareness of active monitoring, regular follow-up, and medication adherence.

Weak risk perception: They rate their subjective health as good and trivialize asymptomatic or mild chronic conditions. They lack understanding of chronic disease indicators such as blood pressure and glucose level, holding the misconception of “no symptoms, no disease.”

Health information remains at the receptive level: Despite exposure to health information through media, lectures, or medical professionals, participants show limited initiative in learning and poor ability to transform knowledge into sustained practice.

Health Service Needs:

Health education interventions that increase risk perception: Strengthen the necessity of chronic disease management during the asymptomatic stage, and intuitively demonstrate changes in indicators such as blood pressure and blood glucose and their potential consequences;

Simple practical tools such as a medication reminder card: Provide simple and sustainable auxiliary tools for health management (e.g., medication reminder cards) to improve the continuity of health behaviors;

Strengthened behavioral guidance from primary care providers: Help individuals translate health recommendations into relatively stable daily management behaviors through regular follow-up visits and repeated reminders.

Representative Quotes:

Patient 14: “My blood pressure can reach over 180, but I still take medicine every day. Sometimes I forget, but taking medicine or not doesn’t seem to make a difference. When I get a headache, I just take an extra pill.”

Patient 21: “Active or not, as long as I don’t feel uncomfortable, it’s fine. For small issues, I just take something myself. My blood pressure and glucose seem okay, so I don’t really care. Old people naturally have minor problems—no need to worry too much.”

Persona 3: Family-Delegated Management

Definition: These older adults delegate the decision-making authority for health management to children or spouses, forming a family-centered passive care model. Their health behaviors are highly externally driven, with limited personal initiative in decision-making and execution.

Portrait Characteristics:

Execution of health behaviors depends on family support: Medication adherence, follow-up visits, and monitoring rely on reminders or accompaniment from family members. Although they participate in health behaviors, they lack understanding of processes, significance, and precautions, acting more as recipients than agents of health management.

Regressive positioning of health roles: Their behavioral patterns fluctuate with the availability of family support resources. When family members migrate for work or their caregiving capacity declines, their health behaviors are prone to interruption or regression, indicating a strong reliance on family care.

Limited expression of health needs: Individuals often prioritize their children’s health over their own, rarely taking the initiative to report discomfort or express health management needs, and show low willingness to actively communicate their health requirements.

Health Service Needs:

Family-oriented health education and empowerment: Provide targeted guidance to family members on chronic disease management, medication supervision, and health monitoring to enhance their caregiving capacity;

Remote support mechanisms: Maintain basic health management behaviors through telephone follow-ups, remote reminders, or community support services when family care is temporarily unavailable;

Strengthened linkage between village doctors and family caregivers: The village doctor serves as a liaison point, regularly communicating with family caregivers to assist in sharing the responsibilities of health management.

Representative Quotes:

Patient 9: “The doctor told me I needed injections. I was afraid and didn’t want to go, but my family kept pushing me, saying I wasn’t taking care of myself. I just followed because they told me to.”

Patient 17: “I never go to the hospital by myself—my son takes me, registers, and buys medicine. When he’s not around, I just endure it. I’m already old; I’ve lived long enough. Children are more important.”

Patient 27: “Everything I know about health comes from a few chats with my daughter. She’s busy—I don’t want to trouble her. I don’t understand these things; rural people don’t know much. Children take care of it.”

Persona 4: Resource-Constrained Striving

Definition: These individuals show clear health awareness and intentions toward chronic disease management and recognize the importance of regular follow-up, monitoring, and medication. However, limited transportation, financial resources, information access, and digital skills result in fragmented and unstable proactive health management.

Portrait Characteristics:

Health behaviors prone to interruption: Participants acknowledge the necessity of chronic disease management and show willingness to undergo check-ups, medical visits, and medication adherence. However, such behaviors are often interrupted due to limited practical conditions, making it difficult to form a stable management routine.

Objective resource constraints as major behavioral barriers: Physical deterioration, lack of transportation, financial burden, digital illiteracy, and inadequate social support impede access to health information, which mainly comes from village doctors or neighbors and is fragmented and unsystematic.

Health Service Needs:

Decentralized health service: Reduce resource constraints such as transportation and mobility barriers through the family doctor contracting system, while ensuring systematic and standardized delivery of health information.

Age-friendly health management tools: Simplify operation procedures of digital health services, and provide simple health management tools such as wearable devices and smart pill boxes.

Increased insurance coverage and economic accessibility: Expand the coverage of medical insurance reimbursement to improve financial accessibility to health services.

Representative Quotes:

Patient 6: “Physical examinations cost money. I want to go, but families like ours can’t afford it. If it’s a free government program, I’ll definitely go. But if I have to pay and travel to the county, then I won’t.”

Patient 11: “I understand what you said about proactive health management being important, but everything is on smartphones now—health info, videos, doctors—but I can’t even use a phone. My kids work away from here, and no one can teach me. It’s not that I don’t want to learn—I just can’t.”

Patient 19: “I can’t walk well, and it’s hard to go out alone. The children work outside and can’t help, and my spouse plays mahjong every day. If someone can take me to the clinic to check my blood pressure or get medicine, I’ll go. If not, I won’t. Even if I want to be active, there’s no one to rely on.”

Persona 5: Actively Learning Management

Definition: These older adults demonstrate relatively high health literacy and strong initiative. They actively acquire, filter, and apply health knowledge. They have gradually developed strong self-management abilities through long-term practice and have the potential to share experience and influence others’ health behaviors within the community.

Portrait Characteristics:

Proactive and planned health management behaviors: They regularly monitor key indicators such as blood pressure and glucose, adjust according to medical advice, and maintain orderly visit schedules and medication adherence, showing planning and self-regulation abilities.

Strong ability to obtain and apply health information: They willingly attend community health lectures, free clinics, and health promotion activities, integrating acquired knowledge into daily routines.

Tendency to share experience and set an example: In family or community interactions, they often take the initiative to share their own health management experience with others, and exert a certain influence on the health perceptions and behavioral choices of other older adults around them.

Health Service Needs:

Continuous support: Provide long-term follow-up and staged guidance to further optimize and improve health behaviors.

More precise and personalized health services: Provide targeted chronic disease management support based on individual health status.

Opportunities to serve as community health facilitators: Empower them as peer supporters to promote the spread of their experience at the community level.

Representative Quotes:

Patient 13: “I go to the town hospital every six months for a check-up. The doctor says I’m doing well. I watch health programs on TV and learn new things. If someone could teach me more about hypertension and diabetes, that would be great. Sometimes, village doctors can’t explain clearly.”

Patient 20: “I take antihypertensive medication on schedule every day and watch health programs. If they teach something useful, I write it down and follow it. I don’t just care about myself—I help neighbors with advice on medicines and where to see doctors.”

Patient 33: “I go to the township hospital for follow-ups. For minor issues, I manage them myself—eat a light diet and check my blood pressure regularly. Clean living and self-discipline are important. One must know how to take care of oneself.”

Discussion

Building on a proactive health management perspective, this study identified five distinct persona types among rural older adults with multiple chronic conditions: “tradition-oriented self-management,” “low perceived risk management,” “family-delegated management,” “resource-constrained striving,” and “actively learning management.” Collectively, these personas form a continuum from traditional/external reliance to autonomous proactive health management, reflecting marked heterogeneity in health cognition, resource accessibility, and behavioral agency. More importantly, they reveal multidimensional sociopsychological and structural mechanisms embedded in rural contexts, including cultural beliefs, family structures, and resource constraints, which contribute to the persistent gap between national proactive health management policy goals and real-world implementation.

“Tradition-oriented self-management” represents a utilitarian view of health deeply rooted in agrarian labor culture. At its core lies a culturally embedded belief system oriented toward the maintenance of functional capacity, in which health is equated with “no pain, eating well, and being able to work.” This perspective may stem from a long-standing reliance on self-sufficiency in regions with limited formal healthcare services (Xin et al., 2020), as well as collective cultural norms emphasizing endurance (Zou, 2021), thrift (S. Zhang et al., 2025), and strong trust in traditional experiential medicine (Pan et al., 2014). Individuals in this segment are the most difficult to engage in proactive health management strategies: their alienation from modern healthcare undermines health education and intervention programs. Government interventions, including the National Basic Public Health Service Plan (NEPHSP; Xiong et al., 2023) and the Healthy China Initiative (Ministry of Science and Technology of the People’s Republic of China, 2017), mainly use biomedical risks (such as blood pressure and blood glucose indicators) as the cognitive basis to stimulate and maintain long-term management. In contrast, the health judgments of this subgroup are grounded in functional experiences and intergenerationally accumulated experiential therapies. Consequently, in the absence of obvious symptoms, they commonly discontinue sustained monitoring of chronic disease control indicators and fail to adhere to standardized follow-up care. They also exhibit resistance to modern medical interventions, and therefore lack intrinsic identification with health education centered on preventive interventions and indicator-based management. This disjunction between cognition and value orientation undermines the effectiveness of policy implementation, leading to the marginalization of routine follow-up care and health education initiatives. Compounded by a lack of culturally sensitive communication strategies and grassroots-level translation mechanisms, intervention measures often fail to resonate with the lived experiences of this population (Xiong et al., 2023). Therefore, in light of the function-oriented health cognition mechanisms characteristic of this group, policy adjustments should prioritize the reconstruction of the meaning framework underpinning health education. Community health workers may translate abstract biomedical indicators into health information directly linked to tangible functional consequences, such as work capacity and the ability to perform activities of daily living, thereby enhancing individuals’ understanding of and identification with the necessity of chronic disease management. Long-term, contextually grounded communication delivered through village clinics and other locally trusted channels, alongside strengthened primary care capacity, is essential to ensure continuity of follow-up and sustained support.

In contrast, “low perceived risk management” occupies a transitional, “semi-acceptance” position between traditional experiential models and modern medicine. Its core characteristic is a mechanism of insufficient chronic disease risk awareness. Although they acknowledge chronic disease diagnoses, they possess superficial understanding and limited internalization of biomedical risk. A higher proportion of male patients with chronic conditions is observed within this type, which may be associated with traditional norms of masculinity—characterized by restraint and a reluctance to actively seek help (Llubes-Arrià et al., 2025). The government’s measures to enhance rural health literacy and early risk identification, including annual health checkups, follow-up visits, and educational activities, have limited effectiveness among this population. They tend to underestimate the potential harm of asymptomatic chronic conditions, and their subjective interpretation of disease severity obscures underlying health risks, resulting in delayed initiation of disease management behaviors. Follow-up visits and diagnostic monitoring are often regarded as “additional tasks after treatment has concluded” rather than as integral components of ongoing disease control. This is manifested in fluctuating medication adherence, symptom-triggered monitoring behaviors, and irregular follow-up attendance. Their behaviors are constrained by limited health literacy (Guo et al., 2024), low risk perception (Z. Zhang et al., 2021), and longstanding patterns of prioritizing acute, symptomatic conditions in care-seeking (Giordano et al., 2025). Accordingly, in response to the delayed behavioral initiation associated with insufficient risk perception in this group, healthcare professionals should construct intervention pathways centered on enhancing risk awareness. Educational tools that integrate contextualized narratives and visualized representations may be developed to facilitate comprehension. Through symptom monitoring, individualized health assessments, and visualized risk prompts, individuals can be guided to adopt the scientific understanding that “management is necessary even in the absence of symptoms,” thereby promoting the establishment of early management and sustained health behaviors (Wu et al., 2023).

While the first two personas are constrained primarily by cognitive and cultural barriers, the “family-delegated management” demonstrates how structural and relational factors shape health behaviors in collectivist rural settings, reflecting a health decision-making mechanism centered on family governance structures. Grounded in Confucian filial piety and traditional intergenerational caregiving norms (Sadeghi et al., 2024). The defining characteristic of this pattern is a lack of proactive cognitive engagement with the disease management process at the individual level, accompanied by limited behavioral control. After their children marry or establish independent households, these older adults often transition from caregivers to “care recipients,” thereby postponing their own needs—for example, adjusting dietary practices according to family preferences, delaying medical appointments until accompanied by someone, or relying on family members to interpret medical information (Bámaca-Colbert et al., 2019). Existing primary care contracting initiatives, including family doctor services (Hendry & Law, 2024) and chronic disease management guidelines (Hui et al., 2024), are highly patient-centered, with follow-up, education, and monitoring directed toward individuals. Family education and caregiver empowerment are seldom prioritized in service packages or performance indicators (Xiong et al., 2023). Labor migration and evolving household structures further reduce familial support, limiting service access and diminishing motivation for self-management (Y. Liu et al., 2021). Consequently, family involvement is often informal, temporary, and uneven, limiting the stability of familial participation in long-term disease management. Given the dominant role of the family within this group’s health governance structure, establishing a multi-level, family-centered support system that integrates community health workers, remote medical platforms, and professional guidance can strengthen the social infrastructure for proactive chronic disease management and ensure continuity of care even in the absence of intimate family support (K. Wang et al., 2024).

On the other end of the continuum is “resource-constrained striving,” a persona primarily governed by a mechanism of structural limitations in resource accessibility. These individuals recognize health risks and express motivation to engage in self-management through medication adherence and regular monitoring. This type is predominantly composed of female patients with chronic conditions, which may be related to the fact that older women often continue to undertake household responsibilities and caregiving roles, thereby compressing the time available for self-monitoring, exercise, and medical consultations (Dwyer et al., 2024). At the same time, their healthcare needs are constrained by structural barriers, including geographic remoteness, inadequate transportation, insufficient coverage of primary healthcare services, and economic vulnerability. These barriers are concretely manifested in interrupted follow-up visits, poor medication adherence, and reliance on external assistance for health monitoring. Taking Sichuan Province, China, as an example, more than 230,000 rural patients must spend over 1 hr traveling to reach a hospital (Q. Wang et al., 2022), and transportation accessibility, including road conditions, public transit availability, and the number of required transfers, strongly influences healthcare utilization among older adults (Li et al., 2022). The scarcity of rural physicians, many of whom have limited formal medical training, further exacerbates disparities (National Health Commission of the People's Republic of China, 2023). Financial constraints persist despite pension reforms, with medication expenditures still constituting a significant share of household health spending (J. Liu et al., 2023). This subgroup illustrates how willingness to engage in proactive health management is weakened by insufficient infrastructure and resource allocation. In light of their behavioral pattern of being “willing but constrained in execution,” intervention strategies for this subgroup should prioritize equitable allocation of healthcare resources and workforce capacity building. In addition, governmental health authorities should promote age-appropriate digital health services, including simplified mobile interfaces, integrated offline-to-online hybrid guidance (Ming et al., 2025), and digital literacy training for village doctors (Chen et al., 2024), which may alleviate resource barriers and improve accessibility to proactive health management.

The most prominent pattern is “actively learning management.” Despite limited resources, individuals in this group actively engage in self-monitoring and make use of accessible health-related channels, reflecting a behavior-driving mechanism centered on health self-efficacy and empowerment. Their behavior suggests that empowerment can emerge when individuals internalize health responsibility and reinterpret limited resources as modifiable rather than deterministic barriers (Lee & Oh, 2020). However, the relatively small group size indicates that proactive health management remains an aspirational ideal constrained by structural realities in rural settings. Importantly, the personas identified in this study are not static; rather, they exhibit fluidity and transformative potential. For example, “resource-constrained striving” may gradually adopt proactive practices following digital skill training and enhanced social support, whereas “low perceived risk management” may revert to traditional health cognition following acute health events or major family changes. Our findings illustrate why one-size-fits-all government policies often fail to achieve optimal health outcomes among rural older adults: they overlook heterogeneity in cultural health beliefs, relational structures, and resource constraints. These policies resonate primarily with a small subset of active managers while failing to engage the majority. Selective participation diminishes scalability and undermines national chronic disease management goals. To improve outcomes, policy design must adopt stratified, dynamic, and multidimensional approaches by simultaneously enhancing capability, creating supportive opportunity structures, and strengthening motivation. These targeted efforts will promote the sustained adoption of proactive health management among rural older adults.

Limitations

However, the findings should be interpreted in light of the study’s limitations. First, the sample was primarily drawn from a single rural region, and its specific cultural context and local healthcare resource conditions may limit the generalizability of the findings, making it difficult to fully represent older adults in diverse rural areas nationwide. Second, as a cross-sectional exploratory analysis, this study was unable to track changes in health behaviors among different profile types following interventions; future longitudinal research is needed to verify the stability of the profiles and differences in their intervention responsiveness. Third, during data collection, in order to enhance the accuracy of participants’ expressions and contextual understanding, some interviews were conducted with the assistance of village doctors who were familiar with the local context. Although written informed consent was obtained prior to all interviews, and participants were explicitly informed that they could refuse to answer any question or terminate the interview at any time, with assurances that the interview content would be used solely for research purposes and anonymized, and were encouraged to express their views freely to minimize potential influence, the presence of village doctors or primary healthcare personnel may nonetheless have influenced participant’ willingness to articulate their views on healthcare services or health-related behaviors. This influence may have been particularly salient when discussing sensitive topics such as medical adherence or service experiences, thereby introducing the possibility of social desirability bias. Future research may further examine data collected in fully independent interview settings in order to compare the potential influence of different interview contexts on the content of participants’ narratives.

Conclusion

From a proactive health management perspective, this study systematically identified and summarized five typical behavioral patterns of proactive health management among rural older adults with multimorbidity. These patterns exhibit pronounced heterogeneity across dimensions, including health cognition, resource accessibility, family support, and action capacity. The findings illuminate the complex mechanisms through which multiple structural and cultural factors intersect to shape proactive health management behaviors within specific rural socio-economic and cultural contexts. This analysis provides contextualized evidence for the persistently low utilization of proactive healthcare services in rural areas and contributes to understanding the underlying reasons why government-led health management programs may demonstrate limited effectiveness in certain rural settings. The findings primarily reflect circumstances in rural regions characterized by relatively insufficient allocation of healthcare resources, ongoing transformation of family support structures, and the continued predominance of traditional health beliefs. Therefore, the applicability of the results is more directly relevant to populations and regions sharing similar socio-economic and cultural backgrounds.

Future health promotion strategies should adopt a persona-based classification approach to implement context-sensitive and stratified interventions. By respecting the socio-cultural characteristics and resource constraints of rural communities, such strategies may facilitate a transition among rural older adults from “passive management” toward a model of “proactive health management.”

Footnotes

Acknowledgements

The authors are grateful to older adults with multimorbidity in rural China who participated in this study.

ORCID iDs

Xiaoge Liu

Haobin Liang

Yingru Huang

Yuexin Huang

Baoting Chen

Jiayu Liu

Yujing Li

Xiaoyu Wan

Ni Gong

Qiaohong Yang

Ethical Considerations

This study strictly adhered to the principles of medical ethics and the guidelines outlined in the Declaration of Helsinki. All participants provided written informed consent voluntarily after being fully informed. Inclusion and exclusion criteria were applied equitably, ensuring that no individual was excluded based on sex, race, religious beliefs, socioeconomic status, or cultural background et al. The present study was approved by the IRB of the School of Medicine, Jinan University (No. JNUKY-2024-0045).

Author Contributions

XGL drafted the main content of the manuscript and participated in the design and revision of the interview outline. NG and QHY served as principal investigators, overseeing the research direction and study design, guiding the entire research process, and ensuring academic rigor. YXH and YJL were responsible for organizing audio materials and transcribing recordings. HBL and YRH assisted in developing the interview outline, reviewing preliminary literature, participating in research discussions, and conducting interviews. JYL, XYW, and BTC managed preparatory work prior to data collection, including participant contact, procurement of materials, and transportation arrangements. All authors contributed to discussions and interpretation of the results, and reviewed and approved the final manuscript.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Guangdong Provincial Philosophical and Social Sciences Planning Project (GD23CSH03); China National University Students Innovation and Entrepreneurship Development Program (CX24438, CX25514); Guangdong Nursing Association & Guangdong Lingnan Nightingale Nursing Research Institute 2026 Annual Scientific Research Project (GDSHLXHYJYY202605).

Declaration of Conflicting Interests

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

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request*.

Author Biographies

Xiaoge Liu, M.S, RN, is a postgraduate at School of Nursing, Jinan University, Guangzhou, Guangdong, CN.

Haobin Liang, M.S, RN, is a postgraduate at School of Nursing, Jinan University, Guangzhou, Guangdong, CN.

Yingru Huang, M.S, RN, is a postgraduate at School of Nursing, Jinan University, Guangzhou, Guangdong, CN.

Yuexin Huang, M.S, RN, is a postgraduate at School of Nursing, Jinan University, Guangzhou, Guangdong, CN.

Baoting Chen, M.S, RN, is a postgraduate at School of Nursing, Jinan University, Guangzhou, Guangdong, CN.

Jiayu Liu, B.S, RN, is a undergraduate at School of Nursing, Jinan University, Guangzhou, Guangdong, CN.

Yujing Li, B.S, RN, is a undergraduate at School of Nursing, Jinan University, Guangzhou, Guangdong, CN.

Xiaoyu Wan, B.S, RN, is a undergraduate at School of Nursing, Jinan University, Guangzhou, Guangdong, CN.

Ni Gong, PhD, is a professor at School of Nursing, Jinan University, Guangzhou, Guangdong, CN.

Qiaohong Yang, Dean, PhD, RN, is a professor at School of Nursing, Jinan University, Guangzhou, Guangdong, CN.

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