Factors associated with healthy lifestyles in Chinese older adults with chronic conditions: A comparison of online and offline social support,mediated by health management self-efficacy and moderated by online patient-centered communication

Abstract

Objective

Chronic conditions represent a critical threat to the quality of life among older adults, owing to their chronicity and often incurable nature. Meanwhile, many older adults face an eHealth social challenge due to technology anxiety and systemic digital exclusion. Grounded in social cognitive theory and socioemotional selectivity theory, this study examines how online and offline social support aid Chinese older adults with chronic conditions, focusing on the mediating role of health management self-efficacy and the moderating role of online patient-centered communication.

Methods

This study is a national quantitative cross-sectional survey conducted in China from June to October 2023 (n = 4,979), targeting older adults with chronic conditions (n = 721). Mediation and moderation analyses were performed using PROCESS macro for SPSS.

Results

Results showed a significant association between offline social support and healthy lifestyle among Chinese older adults with chronic conditions (β=.135, p <.01), whereas the association between online social support and healthy lifestyle was not significant (β=-.034, p>.05). In addition, health management self-efficacy mediated the association between both online and offline social support and healthy lifestyle (effect=.012, 95%CI [.009,.043]; effect=.032, 95%CI [.023,.042]). Furthermore, a moderating effect of online patient-centered communication was observed in the online social support pathway (β=.160, p<.05).

Conclusions

This study provides empirical evidence on the associations between online and offline social support and healthy lifestyle among Chinese older adults with chronic conditions, with health management self-efficacy as mediator and online patient-centered communication as a moderator. Understanding these relationships can inform interventions designed to enhance health communication, foster social connectedness, and promote well-being among older adults.

Keywords

social support healthy lifestyle older adults chronic conditions health management self-efficacy online patient-centered communication

Introduction

Chronic diseases represent a critical threat to the quality of life among older adults due to their chronicity and incurable nature,¹ necessitating long-term and often lifelong health management.² The rapid aging of the global population has led to a higher prevalence of multimorbidity, particularly among community-dwelling older adults, where multiple chronic conditions increasingly coexist.³ For example, data from the China Health and Retirement Longitudinal Study (CHARLS) indicate that the prevalence of chronic disease comorbidity among Chinese middle-aged and older adults surged from 34.3% in 2015 to 59.9% in 2020.⁴ Moreover, national surveys estimate that 81.1% of Chinese older adults have at least one chronic condition, with prevalence peaking at 87.2% among those aged 80-84.⁵ This burden of multimorbidity significantly increases treatment complexity and healthcare costs, while also impairing daily functioning. Research has shown that chronic disease and multimorbidity markedly reduce the capacity to perform both basic and instrumental activities of daily living.^6,7 In addition, patients with multiple chronic conditions experience a greater treatment burden and reduced health-related quality of life.^8,9

In response, health-promoting behaviors, such as balanced nutrition and regular physical activity, are regarded as essential components of chronic disease management.¹⁰ Evidence shows that engaging in physical activity significantly reduces the risks of all-cause and cardiovascular mortality.¹¹ Moreover, diet intervention such as increasing fruit and vegetable intake and reducing sodium consumption, have been shown to lower the risk of stroke.¹² However, older patients often face challenges in independently maintaining these behaviors.¹³ Previous research indicated that among older adults with multiple chronic conditions, limited health knowledge and reduced physical functioning are associated with diminished self-management capacity and poor perceived health status.¹³

Within this context, social support, encompassing both offline¹⁴ and online forms,¹⁵ plays a pivotal role in facilitating health-promoting behaviors. Empirical evidence suggests that social support strengthens self-efficacy, thereby contributing to improved health-promoting behaviors in older adults.^14,15 For older adults with chronic conditions, offline support offers essential emotional sustenance and practical assistance,¹⁶ whereas online support overcomes spatial and temporal barriers by providing access to a wide range of health information and management tools.^16,17 With the rapid growth of digital health in China, older adults are making greater use of online resources and technologies, including wearable devices and mobile health applications.^18,19 However, challenges including the digital divide and social isolation limit the effectiveness of these tools,²⁰ which often fall short of achieving their intended health-promotion goals.^20,21 In addition, prior research indicates that as older adults perceive their future time as limited, their social goals shift from seeking information to prioritizing emotional fulfillment and meaningful engagement.^22–24 Consequently, they often narrow their social networks, maintaining close ties with family members and long-term friends, which may be sustained through both offline and online communication channels, rather than forming new, less familiar or weakly connected relationships.^22–24 In online support contexts, although some assistance is provided by close family members and friends,²⁵ a substantial proportion of interactions often occur among socially distant or unfamiliar network members through digital platforms.^26,27 Compared with support embedded in emotionally close offline relationships, interactions with unfamiliar online users may be less likely to fulfill older adults’ emotionally meaningful goals.²⁸ Consequently, the shift in social motivation among older population’s may influence digital health adoption and affect the effectiveness of online social support in promoting their health management.²⁴ These considerations underscore the need to comprehensively examine the relationship between online and offline social support and healthy lifestyle behaviors among older adults with chronic conditions.

Regarding research gaps, the effectiveness of online support for older adults remains a topic of debate.^24,29 Some studies suggest that online social support can enhance older adults’ well-being,^30,31 while others indicate it may have unintended negative effects, such as increasing anxiety³² or provoking unnecessary fears.³³ In addition, existing research on older adults rarely adopts a comprehensive perspective on social participation that considers both online and offline social support simultaneously,²⁴ often focusing on either online or offline participation, or failing to distinguish between the two.^24,34 Moreover, intervention research on online patient-centered communication among older adults with chronic conditions in China remains limited.³⁵ Building on these research gaps and grounded in social cognitive theory and socioemotional selectivity theory, this study examines and compares the associations of online and offline social support with health-promoting behaviors among older adults with chronic conditions, considering the mediating role of health management self-efficacy and the moderating role of online patient-centered communication. The aim is to inform strategies that support the well-being of older adults managing chronic health conditions. Figure 1 displays the conceptual model of moderated mediation based on this thinking.

Figure 1.

The conceptual model of moderated mediation: Association between online and offline social support and healthy lifestyle among Chinese older adults with chronic conditions, mediated by health management self-efficacy and moderated by online patient-centered communication.

Theoretical framework

Social cognitive theory posits that behavior is shaped by prospective thinking.^36,37 In the context of chronic disease self-management, key cognitive determinants include perceptions of health resources, personal goals, and health self-efficacy.³⁸ According to Bandura’s concept of reciprocal determinism, this study treats health management self-efficacy as an internal personal motivator and social support as an external environmental motivator,³⁹ acknowledging that external support can strengthen internal self-efficacy and promote health behaviors.^40,41

Moreover, this study draws on Socioemotional Selectivity Theory to guide the understanding of perceived external social support among older adults. For older adults living with chronic conditions, the perception of limited lifetime horizons shifts social goals toward emotionally meaningful, present-oriented aims, such as affect regulation and maintaining close relationships, rather than future-oriented objectives like acquiring information or expanding social networks.^22,42 Accordingly, older adults tend to narrow their social circles and prioritize emotionally close ties.^43,44 For older adults, offline interactions may more consistently provide positive emotional feedback due to their immediacy and relational embeddedness,^45–47 whereas a substantial proportion of online social support, particularly loosely structured online communities, tends to emphasize informational or instrumental resources over emotionally meaningful exchanges.^48,49 Moreover, interactions in online communities are more likely to involve weak-tie connections, whereas offline support more commonly stems from close family members and friends, reflecting differences in relational closeness across online and offline contexts.⁵⁰ This theoretical perspective informs our integrative analysis of online and offline social support and our comparative examination of their respective associations with health-promoting lifestyles.

Formulating hypotheses

Social support, a key concept of this study, is conceptualized as perceived functional support, referring to individuals’ perceptions of the availability of informational, emotional, and instrumental resources provided through social relationships.^51,52 Social support is recognized as a motivator that can help individuals progress from the decision-making stage to the action stage.⁵³ In this study, the “action” of chronic disease management refers to healthy lifestyle behaviors. A healthy lifestyle encompasses patterns of living that promote overall physical and mental well-being while mitigating health risks.⁵⁴ Such behaviors include good nutrition, regular physical activity, adequate rest, and stress management.⁵⁴ Prior research has demonstrated that actively modifying personal lifestyle can reduce the incidence of chronic diseases and represents a critical component of health management for older adults with chronic conditions.⁵⁵

Emerging evidence indicates that online social support can facilitate healthier lifestyles among older adults with chronic conditions.^56,57 Previous studies have shown that wearable devices and mHealth applications can increase daily physical activity and improve metabolic outcomes, with interventions targeting older adults demonstrating benefits in step counts, sleep quality, and glycemic control.^58,59 Similarly, WeChat-based community programs for middle-aged and older adults with hypertension have enhanced blood pressure management and self-care behaviors, highlighting the value of online peer education and support.⁶⁰ In parallel, offline social support from family and close social networks remains a key determinant of lifestyle adherence among older adults with chronic conditions. Instrumental support from family, such as medication reminders, shared meal preparation, and accompaniment to medical appointments, has been consistently linked to better medication adherence⁶¹ and healthier dietary practices.^62,63 Moreover, emotional support from spouses and children has been shown to reduce psychological distress,⁶⁴ enhance self-efficacy,⁶⁵ and foster sustained engagement in physical activity.^44,64,65 Accordingly, this study proposed the first and second hypotheses:

H1 Online social support is positively associated with healthy lifestyle among Chinese older adults with chronic conditions.

H2 Offline social support is positively associated with healthy lifestyle among Chinese older adults with chronic conditions.

Health management self-efficacy is conceptualized as an individual’s confidence in their ability to make decisions and undertake actions that lead to positive health outcomes.⁶⁶ This study regards health management self-efficacy as a critical internal factor in the health management of older adults with chronic conditions.³⁹ In contemporary society, the development of digital health has significantly facilitated healthy aging.⁶⁷ Nevertheless, older adults with chronic conditions continue to face substantial barriers, including the digital divide,⁶⁸ social isolation,⁶⁹ and technology-related anxiety.⁷⁰ With this context, social support assumes particular importance.⁷¹ According to social support theory, perceived online and offline support constitutes a form of social capital that older adults can utilize.^41,72 Receiving assistance in achieving health goals through such support reinforces their belief in effectively leveraging this capital within a digital context, thereby enhancing confidence in health management and strengthening self-efficacy.^16,73 For instance, structured online health communities and remote support groups for older adults with chronic conditions, including type 2 diabetes, arthritis and chronic obstructive pulmonary disease, have been shown to facilitate peer modeling and professional encouragement, thereby enhancing participants’ self-efficacy in chronic disease self-management.⁷⁴ In addition, previous studies indicate that the management of older patients with chronic obstructive pulmonary disease (COPD) highlights the role of offline social networks in supporting self-management and enhancing self-efficacy.⁷⁵ Therefore, this study posits the third and fourth hypotheses:

H3 Online social support is positively associated with health management self-efficacy among Chinese older adults with chronic conditions.

H4 Offline social support is positively associated with health management self-efficacy among Chinese older adults with chronic conditions.

The achievement of self-directed change occurs when an individual understands the impact of personal behaviors on well-being, believes in their capacity to influence outcomes through effort and available resources, and is motivated to act accordingly.³⁹ Previous research indicates that chronic disease self-management improved with enhanced self-efficacy.^76,77 Among older adults with chronic conditions, health management self-efficacy is shaped by social capital and support, as recognizing one’s ability to engage in effective self-management through these resources further strengthens self-efficacy,^16,78 which in turn can promote healthier lifestyle behaviors.^79,80 Consequently, self-efficacy may serve as a mediator between social support and health lifestyle in this population. Thus, this study posits the fifth and sixth hypotheses:

H5 Health management self-efficacy is positively associated with healthy lifestyle among Chinese older adults with chronic conditions.

H6 Health management self-efficacy plays a mediating role in the association between online and offline social support and healthy lifestyle among Chinese older adults with chronic conditions.

Additionally, regarding social support, previous research suggests that older adults tend to prioritize offline interactions that provide positive emotional feedback over large, complex online networks, which often involve weaker social ties and potential challenges such as misinformation, technology-related anxiety, and challenges in establishing and maintaining meaningful online relationships.^22,24,70,81 Building on older adults’ preference for emotionally oriented goals^22–24 and the challenges they encounter in online social participation, this study tries to examine the moderating role of online patient-centered communication in shaping the effectiveness of online social support. Within the patient-centered communication approach, the dimension of “effective interpretation and education of health information” enhances the credibility and clarity of information, thereby reducing anxiety stemming from information overload and uncertainty.⁸² Furthermore, the emphasis on emotional responsiveness in patient-centered communication is consistent with older adults’ orientation toward emotionally meaningful interactions.^83,84 Empirical evidence also suggests that online patient-centered communication can strengthen older adults’ confidence in managing their health.^35,85 In addition, prior studies have incorporated patient-centered communication (PCC) as a moderating variable, examining its role in shaping the relationship between online health information use and health outcomes,⁸⁶ as well as between minority stress and psychological well-being.⁸⁷ Therefore, this paper posits the eighth hypothesis:

H7 Online patient-centered communication positively moderates the association between online social support and health management self-efficacy among Chinese older adults with chronic conditions.

Methods

Data sources

This study draws on primary data collected through the Chinese national health survey organized by the corresponding authors’ research team. Research ethics approval for this study was obtained from the first author’s institute, with ethics application reference number SSHRE23-APP013-FSS. This study is a quantitative cross-sectional study implemented with the assistance of Kantar Company between June and October 2023, using quota random sampling method to recruit a total of 4,979 adults from Mainland China. The quotas were set based on age and gender distributions according to the Seventh National Census by the National Bureau of Statistics of China (https://www.stats.gov.cn). Random sampling was then used within each quota cell to reduce sampling bias. Before participation, respondents received an information sheet describing the study’s purpose, the voluntary nature of participation, and the confidentiality of their responses. Written informed consent was obtained from all participants prior to data collection. For the purposes of this study, we focus on a subsample of older adults living with chronic conditions (n = 721).

Sample criterion

This study focuses on older adults with chronic conditions. The first criterion is age. Consistent with the World Health Organization definition and national statistical standards, participants aged 60 years and older were classified as older adults in developing countries.⁸⁸ This criterion is widely used in studies on aging⁸⁹ and chronic conditions studies.⁹⁰ The second criterion aligns with previous studies that include individuals diagnosed with chronic conditions. The presence of chronic conditions was assessed via a self-report checklist of physician-diagnosed diseases. The selection of conditions (including obesity/high blood sugar, hypertension, cardiac problems, chronic lung disease, arthritis) was informed by established comorbidity indices, notably the Elixhauser comorbidity measure,⁹¹ which identifies chronic physical health conditions that are significant drivers of healthcare utilization and outcomes.

Variables and measurements

Healthy lifestyle is an ensemble of behaviors in daily life that help promote health.⁵⁴ It is measured using three questions, originated from the well-established Health-Promoting Lifestyle Profile II (HPLP-II).^92–94 The first question is about sleep, physical activity and social activity. The respondents were asked whether the following behaviors were consistent with their deed circumstances: (a) “Sleep about eight hours a day”; (b) “A regular routine”; (c) “Be active in fitness”; (d) “Have regular exercise”; I “Participate in enjoyable activity”; (f) “Have suitable relaxation time”. (1= not at all to 5 = fully consistent). The other two questions are about healthy eating: “How many fruits do you eat every day?”; “How many vegetables do you eat every day?” (M =3.70, SD =0.72, Cronbach’s alpha=0.84).

Social support is conceptualized in this study as perceived functional support, referring to individuals’ perceptions of the availability of informational, emotional, and instrumental resources provided through social relationships, both online and offline.^52,95 This functional framework is proposed by House⁵² and widely adopted in subsequent health research.^51,96 The operationalization of online and offline social support are informed by the measurement structure of the Medical Outcomes Study Social Support Survey (MOS-SSS) guided by this framework.⁹⁷ For online social support, respondents were asked whether their online health activities make them feel they can: (a) Feel able to find useful health information in online community; (b) When participating in an online community or support group, I feel that I receive practical advice and support from people with similar health concerns; (c) Feel that others in community will provide them with emotional support when they needed. Responses were coded (0=no, 1=yes) to indicate the presence or absence of each type of support, consistent with prior empirical research.^98–100 The three items were summed to create an index (range = 0-3), with higher scores indicating greater perceived availability of online social support (M= 1.77, SD= 0.92, Cronbach’s alpha=0.72). Offline social support, defined as support from others offline, was measured using nine items to reflect the informational, emotional, and instrumental construct. The question was “To what extent do you agree with the following statements”: a) I can talk about health issues with my family; b) my family tries to help me in practice; c) I get emotional help and support from my family; d) I can talk about health issues with my friends; e) my friends try to help me in practice; f) I get emotional help and support from my friends; g) I can talk about health issues with a special person; h) there is a special person who tries to help me in practice; i) I get emotional help and support from this special person (1=never to 5=always). (M= 3.84, SD= 0.82, Cronbach’s alpha=0.82).

Health management self-efficacy is defined as a person’s belief in their ability to effectively manage and maintain their health.^66,101 In this study, health management is operationalized with five items, drawn from Perceived Health Competence Scale¹⁰² and has been employed in subsequent studies including the Perceived Diabetes Self-management Scale.¹⁰³ Respondents were asked to indicate the extent to which the following statements reflected their actual situation: a) I am capable of managing my health conditions when they arise; b) I am skilled at using various aids and tools to make my daily life easier; c) I possess effective skills to help me handle health-related issues; d) I have good strategies for managing health problems; e) I have many effective methods to prevent certain diseases. The answers were measured using a five-point Likert scale (1=strongly disagree to 5=strongly agree). (M=3.74, SD=0.67, Cronbach’s alpha=0.80).

Patient-centered communication is a healthcare approach that focuses on understanding and responding to patients’ needs, preferences, and values, ensuring that patients’ perspectives guide all clinical decisions.^104,105 It involves effective information exchange, emotional support, shared decision-making, and fostering a therapeutic relationship that respects the patient as a whole person.^104,105 In this study, online patient-centered communication (OPCC) refers to patients’ evaluative perceptions of the quality and effectiveness of physician-patient communication delivered via online channels, rather than to the structural or affordance-based features of the platforms (e.g., synchronicity, modality, or continuity). The measurement of perceived online PCC scale is originated from the three sub-constructs from Epstein and Street’s functional model: exchanging information, responding to emotions, and fostering healing relationships.¹⁰⁶ These sub-constructs have also been applied in the Chinese context in previous studies.^107,108 Each construct was assessed with three items. For exchanging information, respondents indicated their level of agreement with the following statements about their experiences communicating with doctors online: a) “My online doctor provided detailed explanations of my health condition”; b) “My online doctor demonstrated a thorough understanding of my health status”; c) “My online doctor’s explanations and recommendations were clear and understandable” (M=1.98, SD=0.62, Cronbach’s α =0.73). For responding to emotions, respondents indicated their agreement with the following statements: d) “My online doctor showed genuine interest in my health”; e) “My online doctor made efforts to reassure and comfort me”; f) “My online doctor was attentive to my feelings” (M=2.05, SD=0.66, Cronbach’s α =0.80). For fostering healing relationships, respondents indicated their agreement with the following statements: g) “My online doctor encouraged me to express concerns and worries”; h) “My online doctor sought my opinions on how to manage my condition”; i) “My online doctor inquired about my views on health” (M=2.03, SD=0.66, Cronbach’s α =0.73). These three core dimensions are consistent with theoretical foundation of Socioemotional Selectivity Theory in this study, which posits that older adults prioritize emotionally meaningful goals, this operationalization emphasizes emotionally meaningful and patient-centered responsiveness in online medical communication contexts.^43,109 Responses were recorded on a 5-point Likert scale (1 = Strongly Disagree; 2 = Disagree; 3 = Neutral; 4 = Agree; 5 = Strongly Agree). Higher scores indicated a greater perception of patient-centered communication with online physicians. (M=2.03, SD=0.56, Cronbach’s α =0.85).

Sociodemographic variables were used to reduce the confounding effects, including age (ranging from 60 to 74), education level (1= primary and below to 7= bachelor’s degree or above), birth gender (male and female) and annual household income (1= ¥30,000 to ¥80,000 to 5= ¥1000,000 or above). To better control the potential variables that could influence main variables, related variable psychological distress was included in this study. Previous study indicated that psychological distress is associated with social support¹¹⁰ and health self-efficacy among chronic disease patients.¹¹¹ The psychological distress variable was measured using a 4-item scale derived from the Patient Health Questionnaire-4,¹¹² a screening tool that assesses core symptoms of depression and anxiety and has been widely used in health research.^113,114 Participants were asked to indicate how frequently, over the past two months, they had been troubled by the following issues: (a) having little interest or pleasure in activities, (b) feeling down, depressed, or hopeless, (c) feeling nervous, anxious, or on edge, and (d) being unable to stop or control worrying. (M=1.80, SD=0.63, Cronbach’s α = .80).

Statistical methods

All analyses were conducted using SPSS version 28. First, descriptive analyses were processed to show the characteristics of the main variables. Second, Pearson correlation analysis was used to explore associations among the core variables. Third, before conducting PROCESS macro analysis, a multicollinearity check was conducted for the three key predictors (online and offline social support, and health management self-efficacy) in the regression predicting healthy lifestyle.^115,116 Fourth, the proposed mediation was tested using the PROCESS macro (Model 4) in SPSS.¹¹⁷ To address potential overlap between online and offline social support, each support index was controlled for when estimating the effects of the other in the PROCESS analyses. The PROCESS macro, developed by Andrew Hayes, is an added-on tool for SPSS that supports advanced statistical methods including mediation and moderation analyses.¹¹⁷ Indirect effects were evaluated using the bias-corrected bootstrap method with 5,000 resamples, and effects were deemed significant when their 95% confidence intervals did not include zero.¹¹⁸ Additionally, PROCESS model 7 is used to test the moderation effect of online patient-centered communication on the association between online social support and health management self-efficacy among older adults with chronic conditions.

Results

Descriptive data

A total of 721 Chinese respondents who were over 60 years old and have chronic disease were involved in this study. Table 1 presents the descriptive characteristics of variables. Statistic shows that the respondents are around 64 years old and 30% of responders receive bachelor’s degree or above. In addition, 45.9% of respondents are women and 54.1% are male.

Table 1.

Descriptive statistics of the independent, dependent, mediating and controlling variables (n = 721; sub-group: older adults with chronic conditions).

Dependent variable	Scale
Healthy Lifestyle	3.70 ± 0.72
Independent variable
Online social support in health communication	1.77 ± 0.92
Offline social support in health communication	3.84 ± 0.85
Mediating variable
Health management self-efficacy	3.74 ± 0.67
Moderating variable
Online patient-centered communication	2.03 ± 0.56
Sociodemographic controls
Age (year, M ± SD)	63.59 ± 3.05
Education (no.%)
Primary and below	30 (4.2)
Junior high school	86 (11.9)
Senior high school	220 (30.5)
Junior college	169 (23.4)
Bachelor’s degree or above	216 (30.0)
Gender
Female	331 (45.9)
Male	390 (54.1)
Annual household income (¥)
¥30,000 to ¥80,000	99 (13.7)
¥80,000 to ¥150,000	218 (30.2)
¥150,000 to ¥500,000	325 (45.1)
¥500,000 to ¥1000,000	68 (9.4)
¥1000,000 or above	11 (1.5)
Psychological distress	1.80 ± 0.63
N	721

SD: standard deviation; M: mean; N/A: not applicable.

Relationships among key variables

Bivariate correlations among key variables are shown in Table 2, with correlation coefficients ranging from -.218 to .229. In addition, A multicollinearity check was conducted for the three key predictors (online social support, offline social support, and health management self-efficacy) in the regression predicting healthy lifestyle. VIF values ranged from 1.004 to 1.034, well below the common cutoff of 10, tolerance values were from 0.967 to 0.996, the maximum condition index was 14.92, which falls within the range sometimes considered indicative of potential multicollinearity but does not indicate a problematic level.¹¹⁹ Importantly, no dimension showed multiple predictors with variance proportions exceeding 0.50, suggesting that multicollinearity was unlikely to substantially bias the regression estimates.¹¹⁹ Therefore, all three predictors were retained in the model.

Table 2.

Zero-order Pearson correlations (n = 721).

Variables	1 HLS	2 ONSS	3 OFFSS	4 SEF	5 OPCC	6 AGE	7 EDU	8 ICM	9 PD
1.Healthy Lifestyle (HLS)	—	-.033	.206**	.229**	-.041*	.093**	.100**	.161**	-.218**
2.Online social support (ONSS)		—	.196**	.084**	-0.033	-0.007	0.037	.093**	.066**
3.Offline social support (OFFSS)			—	.138**	-.059**	.133**	.068**	.165**	-.063**
4.Health management self-efficacy (SEF)				—	0.323	0.151	.101**	.038*	-.139**
5.Online patient-centered communication (OPCC)					—	.086**	-0.014	-.091**	-0.007
6.Age (AGE)						—	-.329**	-0.009	-.100**
7.Education (EDU)							—	.188**	-0.009
8. Income (ICM)								—	-.104**
9. Psychological Distress									—

Association among online social support, offline social support, health management self-efficacy and healthy lifestyle

H1 predicted that online social support is positively associated with healthy lifestyle among Chinese older adults with chronic conditions. As depicted in Table 3 and Figure 2, the positive association was not statistically significant (β=-.034, p>.05). H1 was not supported.

Table 3.

Summary of mediation effects (n = 721).

Mediation pathway	b	SE	95%CI
ONSS → SEF	.0850*	.0153	[.0351, .0950]
OFFSS → SEF	.1350**	.0180	[.0996, .1703]
SEF→ HLS	.2262***	.0158	[.1952, .2573]
ONSS → HLS (direct)	-.0343	.0244	[-.0821, .0135]
OFFSS → HLS (direct)	.2044**	.0149	[.1751, .2337]
ONSS → SEF → HLS	.0115	.0099	[.0088, .0431]
OFFSS → SEF→ HLS	.0324	.0047	[.0233 .0420]
Moderation pathway
ONSS x OPCC	.1602*	.0681	[.0268, .2937]

Note. b is regression unstandardized coefficients B and bp is percentage coefficients

Figure 2.

Association between online and offline social support and healthy lifestyle mediated by health management self-efficacy among Chinese older adults with chronic conditions.

H2 proposed that offline social support is positively associated with healthy lifestyle among Chinese older adults with chronic conditions. Table 3 shows that the positive association was statistically significant (β=.204, p<.01). H2 was supported.

H3 predicted that online social support is positively associated with health management self-efficacy among Chinese older adults with chronic conditions. In accordance with this, Table 3 shows that the positive association was statistically acknowledged (β=.085, p<.05). H3 was supported.

H4 proposed that offline social support is positively associated with health management self-efficacy among Chinese older adults with chronic conditions. As depicted in Table 3, the association was statistically significant (β=.135, p<.01). H4 was supported.

H5 predicted that health management self-efficacy is positively associated with healthy lifestyle among Chinese older adults with chronic conditions. Table 3 shows that the positive association was statistically significant (β=.226, p<.001). H5 was supported.

H6 proposed that health management self-efficacy mediates the association between online and offline social support and healthy lifestyle among Chinese older adults with chronic conditions. Results in Table 3 showed that the indirect effect was statistically significant (effect=.012, 95%CI [.009, .043]; effect=.032, 95%CI [.023, .042]), supported by the bootstrapping results. H6 was supported.

H7 predicted that online patient-centered communication moderates the association between online social support and health self-efficacy. As depicted in Table 3, the moderation effect was statistically significant (β=.160, p<.05). Figure 3 shows that the slope for individuals with high level of online patient-centered communication is steeper than that for those with low levels. The association between online social support and health management self-efficacy was stronger at higher levels of online patient-centered communication among Chinese older adults with chronic conditions. H7 was supported.

Figure 3.

The moderation effect of OPCC on the association between online social support and health management self-efficacy among Chinese older adults with chronic conditions.

Discussion

This study examines the direct and indirect associations between online and offline social support and healthy lifestyle among Chinese older adults with chronic conditions, with particular attention to the mediating role of health management self-efficacy. Furthermore, online patient-centered communication is included in the online support pathway as a moderating factor. These findings not only explain how online and offline social support are associated with healthy lifestyle, but also extend the application of social cognitive theory and socioemotional selectivity theory to the context of chronic disease management among Chinese older adults. The following section provides a detailed discussion of the results.

Significant mediation and non-significant direct association in online support pathway

Echoing previous research, this study replicated positive associations between offline social support and healthy lifestyle,^61–64,120 as well as between health management self-efficacy and healthy lifestyle among older adults with chronic conditions.^77,121,122 In addition, the findings demonstrate the mediating role of health management self-efficacy in the relationship between social support and healthy lifestyle in this population. It is noteworthy that the direct effect of online social support on a healthy lifestyle was not significant; however, health management self-efficacy significantly mediated this association. Importantly, this finding should not be interpreted as evidence that online social support is inherently ineffective or emotionally superficial for older adults. Rather, it suggests that the effectiveness of online social support operates through more indirect and conditional pathways. In this study, online social support was measured as the perceived ability to obtain useful health information, participate in peer communities, and receive emotional support when needed. These elements reflect informational, instrumental, and emotional availability rather than direct behavioral guidance. As such, they may not immediately translate into routine behaviors such as regular exercise or sleep. Instead, they are more likely to strengthen individuals’ confidence in managing health conditions, using appropriate tools, and applying effective strategies, which are core dimensions of health management self-efficacy. For instance, perceiving access to useful information may enhance confidence in handling symptoms or making dietary decisions,¹²³ while interaction with people with similar health concerns may provide social modeling¹²⁴ and emotional reassurance might reduce uncertainty.¹²⁵ Through these processes, online social support operates as a cognitive and motivational resource that enhances perceived competence in health management. For older adults with chronic conditions, access to such support alone is insufficient. What proves essential is their perceived self-efficacy, sense of empowerment and the emotional significance they attach to these experiences.¹⁴ When older adults recognize their capacity to effectively use online social support, their confidence in managing chronic conditions increases, which is associated with greater engagement in health-promoting behaviors.^16,78

These results align with Socioemotional Selectivity Theory (SST), which emphasizes that older adults’ social preferences are guided by goal orientations rather than by communication media itself.^22–24 As perceived future time becomes limited, older adults increasingly prioritize emotionally meaningful, present-oriented goals over future-oriented goals such as information accumulation or network expansion.^22,23,81 Within this framework, online and offline social support should be understood as differing in their dominant functional emphases rather than representing mutually exclusive or homogeneous categories. While offline interactions may more consistently facilitate emotionally meaningful exchanges due to their immediacy and relational embeddedness,¹²⁶ emotionally close and meaningful support can also be delivered through online channels, particularly when interactions involve close family members or long-standing social ties (e.g., intergenerational communication via WeChat).¹²⁷ Therefore, the absence of a direct effect does not diminish the potential value of online social support; instead, it underscores the importance of psychological mechanisms, including health management self-efficacy, that enable older adults to translate online support into concrete health behaviors.

Positive moderation effect of online patient-centered communication

Although the direct effect was not significant, the positive indirect associations between online social support and healthy lifestyle through health management self-efficacy highlights the potential benefits of online support. Furthermore, this study identified a positive moderating role of online patient-centered communication within the online support pathway. This finding aligns with Socioemotional Selectivity Theory, which posits that when lifetime horizons are perceived as limited, older adults increasingly prioritize emotionally meaningful goals.^22,23 In the context of online physician-patient communication, patient-centered communication delivered by online doctors is more likely to meet older patients’ needs to feel understood, reassured, and emotionally supported.¹²⁸ In addition, the moderating effect of OPCC in this study highlights the value of authoritative, patient-centered communication in shaping how community-based online support is interpreted and utilized. Although online social support from communities members often provides experiential advice and emotional encouragement, the medical appropriateness and individual applicability of such information may be uncertain.¹²⁹ When OPCC is of high quality, online doctors can respond to patients’ concerns, clarify misunderstandings, and offer structured and personalized guidance.^130,131 This process enables patients to integrate online community-based information with professional medical advice, thereby enhancing their confidence in managing their health. Additionally, this interpretation is further supported by prior research using HINTS data, which demonstrated that online patient-centered communication was positively associated with higher self-efficacy among patients with chronic conditions.¹³² Therefore, these findings suggest that the perceived quality of patient-centered physician communication through online channels may strengthen the positive association between online social support and health management self-efficacy among Chinese older adults with chronic conditions, thereby contributing to better overall health and well-being.

Practical implication

In terms of practical implications, firstly, this study underscores the importance of both online and offline social support for older adults with chronic conditions. Family members, friends, and online communities should engage in timely communication with patients about their health issues and give them information support, as well as participate in their daily routine of health management to give them emotional support such as a sense of companionship and reassurance. Secondly, the findings indicate a significant indirect association between online social support derived from online communities and a healthy lifestyle via health management self-efficacy, whereas the direct association was not significant. This finding suggests that simply providing online support may be insufficient, and its benefits rely on enabling older adults to effectively use the support, which builds their self-efficacy and internal motivation for managing their conditions. Accordingly, the design and implementation of health interventions should prioritize empowering patients through the creation of a supportive environment. For example, to enhance older adults’ perceived ability to obtain useful health information, communities should provide curated and clearly organized content, such as pinned evidence-based posts, simplified symptom-management guides, and searchable Q&A sections addressing common concerns (e.g., medication use, diet, exercise).⁴⁸ Short instructional videos demonstrating home-based practices may further translate information into actionable skills, strengthening users’ confidence in applying effective strategies.¹³³ To leverage peer interaction, communities can incorporate structured experience-sharing threads or highlight members’ coping strategies to facilitate social modeling. Such visibility of practical success may reinforce beliefs in older adults’ own ability to manage chronic conditions.¹³⁴ Thirdly, this study highlights the moderating role of online patient-centered communication, suggesting that online support from community may be most effective when complemented by structured physician involvement that embodies patient-centered communication. For example, in a physician-driven social media group for older adults with chronic conditions, physicians could periodically review common questions, provide clear and accessible explanations, and clarify misunderstandings arising from experiential advice shared by community members.¹³⁵ Beyond clarification, physicians can encourage patients to articulate concerns and reflect on how recommended strategies fit their daily lives. In this way, online patient-centered communication strengthens older adults’ confidence in symptom management, practical tool use, and prevention. In addition, given the large number of older adults in China who regularly watch short dramas on the Douyin platform (Chinese TikTok), health education by healthcare professions on chronic disease management could be integrated into short-video storytelling. In practice, it is vital not only to provide support, but it is also crucial to consider how to facilitate the effective use of this support. Achieving this requires disseminating health knowledge, strengthening oversight of online health information, and improving patients’ health literacy, but most importantly, carefully addressing the genuine needs and concerns of older adults through appropriate responses in medical practice.

Limitations and future research

This study has several limitations. Firstly, the cross-sectional design of the study restricts the ability to establish causal relationships to gain a more accurate understanding of the causal links among social support, health management self-efficacy and healthy lifestyle, experimental research and panel research are essential. Secondly, for the measurement of variables, although both online and offline social support were operationalized based on functional support dimensions, the measurement of offline support is necessarily more concrete due to the identifiable nature of offline relationships (e.g., family and friends), whereas online support reflects more diffuse and community-based interactions. Future research could benefit from more parallel measurement instruments across online and offline contexts. In addition, further exploration through in-depth interviews is proposed, focusing on specific aspects of information and emotional support within online and offline social support for a more nuanced and comprehensive investigation. Thirdly, the operationalization of online patient-centered communication in this study captures patients’ perceived communication quality delivered via online channels, but does not assess the structural or affordance-related characteristics of online interaction (e.g., synchronicity, platform modality, continuity of communication, or engagement patterns). Future research could incorporate measures of online interaction affordances to better distinguish between communication quality and platform-based interaction processes. Fourthly, the sample size was determined by feasibility considerations and data availability rather than a formal a priori power calculation. Nevertheless, the final sample of older adults with chronic condition (N = 721) exceeds the sizes typically required for regression-based mediation analyses. Future studies should incorporate formal power analyses at the design stage to improve methodological planning and transparency. Finally, this study regards health management self-efficacy as a significant mediator in the relationship between social support and healthy lifestyle among older adults with chronic conditions, potentially overlooking other pertinent intervening variables. Future research should strive to bridge this research gap by investigating factors such as socioeconomic, family relationships, healthy lifestyle cognitive beliefs and social exclusion.

Conclusion

Drawing on social cognitive theory and socioemotional selectivity theory, this study provides empirical evidence regarding the relationships between online and offline social support and healthy lifestyle among Chinese older adults with chronic conditions. Our findings indicate offline social support is positively associated with healthy lifestyle, whereas online support shows no statistically significant association. Health management self-efficacy significantly mediated the associations of both online and offline social support with healthy lifestyle. Furthermore, online patient-centered communication significantly moderated the association between online social support and health management self-efficacy. Recognizing these relationships can inform the design of interventions aimed at strengthening health communication, fostering social connectedness, and promoting overall well-being in older populations.

Supplemental material

Supplemental material - Factors associated with healthy lifestyles in Chinese older adults with chronic conditions: A comparison of online and offline social support, mediated by health management self-efficacy and moderated by online patient-centered communication

Supplemental material for Factors associated with healthy lifestyles in Chinese older adults with chronic conditions: A comparison of online and offline social support, mediated by health management self-efficacy and moderated by online patient-centered communication by Qingqing Xie, YingXia Zhu and Xinshu Zhao in Digital Health.

Footnotes

ORCID iDs

Qingqing Xie

YingXia Zhu

Ethical considerations

Research ethics approval for this study was obtained from the Institutional Review Board of the University of Macau, with application reference number SSHRE23-APP013-FSS. Before the survey, each participant was provided with a participant information sheet that explained the purpose of the study, the voluntary nature of participation, and the confidentiality of their responses.

Consent to participate

An informed consent sheet was obtained from all participants before their involvement in the study.

Author contributions

The author contributions for this manuscript are as follows: Qingqing Xie conceptualized the model and wrote the original draft of the manuscript. YingXia Zhu contributed to method. Xinshu Zhao served as the guarantor for the research.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported in part by grants of University of Macau, including CRG2021-00002-ICI, ICI-RTO-0010-2021, Xinshu Zhao PI; Macau Higher Education Fund, HSS-UMAC-2020-02, Xinshu Zhao PI.

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 are available from the corresponding author upon reasonable request.*

Use of artificial intelligence

During the preparation of this manuscript, the authors used the AI tool ChatGPT (OpenAI) to assist with improving the clarity and readability of the language. No substantive content, results, or conclusions were generated by AI, and the authors take full responsibility for all content.

Supplemental material

Supplemental material for this article is available online.

References

Weingarten

Henning

Badamgarav

, et al. Interventions used in disease management programmes for patients with chronic illnesswhich ones work? Meta-analysis of published reports. Bmj 2002; 325: 925. https://doi.org/10.1136/bmj.325.7370.925

Crombet

Lage

. Immunotherapy for transforming advanced cancer into a chronic disease: How far are we? In: Immune Rebalancing. Elsevier, 2016, pp. 105–120.

Chowdhury

Das

Sunna

, et al. Global and regional prevalence of multimorbidity in the adult population in community settings: a systematic review and meta-analysis. EClinicalMedicine 2023; 57: 1–22.

Zhang

Cai

, et al. The evolution of comorbidities in chronic diseases among Chinese middle-aged and elderly people: Evidence from the CHARLS (2015-2020). PLoS One 2025; 20: e0329372. https://doi.org/10.1371/journal.pone.0329372

Xie

, et al. Chronic disease in China: geographic and socioeconomic determinants among persons aged 60 and older. Journal of the American Medical Directors Association 2023; 24: 206–212.e205. https://doi.org/10.1016/j.jamda.2022.10.002

Chen

Shen

, et al. Chronic disease and multimorbidity in the Chinese older adults’ population and their impact on daily living ability: a cross-sectional study of the Chinese Longitudinal Healthy Longevity Survey (CLHLS). Archives of public health 2024; 82: 17. https://doi.org/10.1186/s13690-024-01243-2

Liang

Wei

, et al. Impacts of chronic diseases and multimorbidity on health-related quality of life among community-dwelling elderly individuals in economically developed China: evidence from cross-sectional survey across three urban centers. Health and Quality of Life Outcomes 2024; 22: 91. https://doi.org/10.1186/s12955-024-02309-z

Gebreyohannes

Gebresillassie

Mulugeta

, et al. Treatment burden and health-related quality of life of patients with multimorbidity: a cross-sectional study. Quality of Life Research 2023; 32: 3269–3277. https://doi.org/10.1007/s11136-023-03473-3

Lee

Shin

, et al. Treatment burden in multimorbidity: an integrative review. BMC Primary Care 2024; 25: 352. https://doi.org/10.1186/s12875-024-02586-z

10.

Miller

Nowson

Dunstan

, et al. Recruitment of older adults with type 2 diabetes into a community-based exercise and nutrition randomised controlled trial. Trials 2016; 17: 467. https://doi.org/10.1186/s13063-016-1589-5

11.

Duncombe

Nemoto

, et al. Physical activity trajectories and accumulation over adulthood and their associations with all-cause and cause-specific mortality: a systematic review and meta-analysis. British journal of sports medicine 2025; 59: 1228–1241. https://doi.org/10.1136/bjsports-2024-109122

12.

Guo

Zhu

Tian

, et al. Role of diet in stroke incidence: an umbrella review of meta-analyses of prospective observational studies. BMC medicine 2022; 20: 194. https://doi.org/10.1186/s12916-022-02381-6

13.

Bayliss

Ellis

Steiner

. Barriers to self-management and quality-of-life outcomes in seniors with multimorbidities. The Annals of Family Medicine 2007; 5: 395–402. https://doi.org/10.1370/afm.722

14.

Sheng

. Social support network, social support, self-efficacy, health-promoting behavior and healthy aging among older adults: A pathway analysis. Archives of gerontology and geriatrics 2019; 85: 103934. https://doi.org/10.1016/j.archger.2019.103934

15.

Choi

. Association of eHealth use, literacy, informational social support, and health-promoting behaviors: mediation of health self-efficacy. International journal of environmental research and public health 2020; 17: 7890. https://doi.org/10.3390/ijerph17217890

16.

Andreou

Dhand

Vassilev

, et al. Understanding online and offline social networks in illness management of older patients with asthma and chronic obstructive pulmonary disease: mixed methods study using quantitative social network assessment and qualitative analysis. JMIR Formative Research 2022; 6: e35244. https://doi.org/10.2196/35244

17.

Hepburn

Williams

McCann

. Barriers to and facilitators of digital health technology adoption among older adults with chronic diseases: updated systematic review. JMIR aging 2025; 8: e80000. https://doi.org/10.2196/80000

18.

Zhang

. Smartphones and telemedicine for older people in China: opportunities and challenges. Digital health 2022; 8: 20552076221133695. https://doi.org/10.1177/20552076221133695

19.

Zhang

Xie

Qiu

, et al. Willing or reluctant to share health data? A moderated mediation analysis of wearable device usage and data-sharing intentions among older adults. Digital Health 2025; 11: 20552076251337948. https://doi.org/10.1177/20552076251337948

20.

Bertolazzi

Quaglia

Bongelli

. Barriers and facilitators to health technology adoption by older adults with chronic diseases: an integrative systematic review. BMC public health 2024; 24: 506. https://doi.org/10.1186/s12889-024-18036-5

21.

Wilson

Heinsch

Betts

, et al. Barriers and facilitators to the use of e-health by older adults: a scoping review. BMC public health 2021; 21: 1556. https://doi.org/10.1186/s12889-021-11623-w

22.

Carstensen

Isaacowitz

Charles

. Taking time seriously: a theory of socioemotional selectivity. American psychologist 1999; 54: 165–181. https://doi.org/10.1037//0003-066x.54.3.165

23.

Löckenhoff

Carstensen

. Socioemotional selectivity theory, aging, and health: The increasingly delicate balance between regulating emotions and making tough choices. Journal of personality 2004; 72: 1395–1424. https://doi.org/10.1111/j.1467-6494.2004.00301.x

24.

Jiang

Head

, et al. Synergistic impacts of online and offline social participation on older adults’ subjective well-being: evidence from the Canadian Longitudinal Study on Aging. European Journal of Information Systems 2024; 33: 699–716. https://doi.org/10.1080/0960085x.2023.2229283

25.

Song

Zhang

. The relationship between WeChat use by Chinese urban older adults living alone and their subjective well-being: The mediation role of intergenerational support and social activity. Psychology Research and Behavior Management 2021; 14: 1543–1554. https://doi.org/10.2147/PRBM.S330827

26.

Wright

Rains

Banas

. Weak-tie support network preference and perceived life stress among participants in health-related, computer-mediated support groups. Journal of Computer-Mediated Communication 2010; 15: 606–624. https://doi.org/10.1111/j.1083-6101.2009.01505.x

27.

Wright

Dawson

Parkinson

, et al. A qualitative study investigating users’ perspective of bariatric surgery online health communities in facilitating social support. Australian Journal of Psychology 2024; 76: 2292022. https://doi.org/10.1080/00049530.2023.2292022

28.

Kim

Shen

. Connecting activities on Social Network Sites and life satisfaction: A comparison of older and younger users. Computers in human behavior 2020; 105: 106222. https://doi.org/10.1016/j.chb.2019.106222

29.

Wang

Yue

, et al. Internet-based healthcare services use patterns and barriers among middle-aged and older adults in China: a cross-sectional study. BMC Public Health 2025; 25: 2188. https://doi.org/10.1186/s12889-025-23338-3

30.

Ellison

McCammon

, et al. Mapping the two levels of digital divide: Internet access and social network site adoption among older adults in the USA. Information, communication & society 2016; 19: 1445–1464. https://doi.org/10.1080/1369118x.2015.1109695

31.

von Humboldt

Mendoza-Ruvalcaba

Arias-Merino

, et al. Smart technology and the meaning in life of older adults during the Covid-19 public health emergency period: a cross-cultural qualitative study. International Review of Psychiatry 2020; 32: 713–722. https://doi.org/10.1080/09540261.2020.1810643

32.

Hunsaker

Nguyen

Fuchs

, et al. Unsung helpers: Older adults as a source of digital media support for their peers. The Communication Review 2020; 23: 309–330. https://doi.org/10.1080/10714421.2020.1829307

33.

Brashier

Schacter

. Aging in an era of fake news. Current directions in psychological science 2020; 29: 316–323. https://doi.org/10.1177/0963721420915872

34.

Chang

Choi

Bazarova

, et al. Age differences in online social networking: Extending socioemotional selectivity theory to social network sites. Journal of broadcasting & electronic media 2015; 59: 221–239. https://doi.org/10.1080/08838151.2015.1029126

35.

Rosenberg

. Patient centeredness, independent health-related Internet use, and online communication with healthcare providers in later life: A cross-sectional study. Patient Education and Counseling 2023; 117: 107971. https://doi.org/10.1016/j.pec.2023.107971

36.

Bandura

. Social foundations of thought and action. Prentice-Hall, 1986, p. 2.

37.

Luszczynska

Schwarzer

. Social cognitive theory. Fac Health Sci Publ 2015; 2015: 225–251. https://doi.org/10.1017/9781108677318.003

38.

Anderson

Winett

Wojcik

. Self-regulation, self-efficacy, outcome expectations, and social support: social cognitive theory and nutrition behavior. Annals of behavioral medicine 2007; 34: 304–312. https://doi.org/10.1007/BF02874555

39.

Bandura

. Health promotion by social cognitive means. Health education & behavior 2004; 31: 143–164. https://doi.org/10.1177/1090198104263660

40.

Albal

Kutlu

. The relationship between the depression coping self-efficacy level and perceived social support resources. Journal of Psychiatric Nursing 2010; 1: 115–120.

41.

Stefanone

Kwon

Lackaff

. Exploring the relationship between perceptions of social capital and enacted support online. Journal of Computer-Mediated Communication 2012; 17: 451–466. https://doi.org/10.1111/j.1083-6101.2012.01585.x

42.

Blanchard-Fields

Chen

Norris

. Everyday problem solving across the adult life span: influence of domain specificity and cognitive appraisal. Psychology and aging 1997; 12: 684–693.

43.

Charles

Mather

Carstensen

. Aging and emotional memory: the forgettable nature of negative images for older adults. Journal of Experimental Psychology: General 2003; 132: 310–324. https://doi.org/10.1037/0096-3445.132.2.310

44.

Radjenovic

Rupprecht

Nikitin

. Establishing and Maintaining Social Relationships During Significant Life Events: The Role of Age. The Journals of Gerontology, Series B: Psychological Sciences and Social Sciences 2024; 79: gbae144. https://doi.org/10.1093/geronb/gbae144

45.

Reblin

Uchino

. Social and emotional support and its implication for health. Current opinion in psychiatry 2008; 21: 201–205. https://doi.org/10.1097/YCO.0b013e3282f3ad89

46.

Momtaz

Haron

Ibrahim

, et al. Social embeddedness as a mechanism for linking social cohesion to well-being among older adults: moderating effect of gender. Clinical interventions in aging 2014; 9: 863–870. https://doi.org/10.2147/CIA.S62205

47.

Cone

Lee

. Older adult communication types and emotional well-being outcomes during COVID-19 pandemic. BMC geriatrics 2023; 23: 178. https://doi.org/10.1186/s12877-023-03856-8

48.

Zhao

Song

. Online health information seeking behaviors among older adults: systematic scoping review. Journal of medical internet research 2022; 24: e34790. https://doi.org/10.2196/34790

49.

Liu

Zhang

, et al. Understanding online health information seeking behavior of older adults: a social cognitive perspective. Frontiers in public health 2023; 11: 1147789. https://doi.org/10.3389/fpubh.2023.1147789

50.

Kim

Katagiri

. Active use of online community reduces loneliness among older adults through weak ties formation. Innovation in Aging 2023; 7: 843. https://doi.org/10.1093/geroni/igad104.2719

51.

Cai

Huang

Yuan

. Profiles of instrumental, emotional, and informational support in Chinese breast cancer patients undergoing chemotherapy: a latent class analysis. BMC Women’s Health 2021; 21: 183. https://doi.org/10.1186/s12905-021-01307-3

52.

House

. Work stress and social support. Addison-Wesley series on occupational stress, 1983.

53.

Palank

. Determinants of health-promotive behavior: A review of current research. Nursing clinics of North America 1991; 26: 815–832.

54.

Adams

Katz

Shenson

. A healthy lifestyle composite measure: Significance and potential uses. Preventive medicine 2016; 84: 41–47. https://doi.org/10.1016/j.ypmed.2015.12.005

55.

Loef

Walach

. The combined effects of healthy lifestyle behaviors on all cause mortality: a systematic review and meta-analysis. Preventive medicine 2012; 55: 163–170. https://doi.org/10.1016/j.ypmed.2012.06.017

56.

Garza

Pauly

Choi

, et al. Everyday Social Support for Health Behaviours in Older Adults during Times of Challenge: Evidence from Daily Life Assessments. Canadian Journal on Aging /La Revue canadienne du vieillissement 2024; 44(2): 1–10.

57.

Lin

Zhu

Wang

, et al. The impact of perceived social support on chronic disease self-management among older inpatients in China: The chain-mediating roles of psychological resilience and health empowerment. BMC geriatrics 2025; 25: 1–13. https://doi.org/10.1186/s12877-025-05902-z

58.

Wang

, et al. Wearable Activity Tracker–Based Interventions for Physical Activity, Body Composition, and Physical Function Among Community-Dwelling Older Adults: Systematic Review and Meta-Analysis of Randomized Controlled Trials. Journal of Medical Internet Research 2025; 27: e59507. https://doi.org/10.2196/59507

59.

Azevedo

RFL

Varzino

Steinman

, et al. Evaluating Effectiveness of mHealth Apps for Older Adults With Diabetes: Meta-Analysis of Randomized Controlled Trials. Journal of Medical Internet Research 2025; 27: e65855. https://doi.org/10.2196/65855

60.

Chen

, et al. A WeChat-based self-management intervention for community middle-aged and elderly adults with hypertension in Guangzhou, China: a cluster-randomized controlled trial. International journal of environmental research and public health 2019; 16: 4058. https://doi.org/10.3390/ijerph16214058

61.

Mondesir

Carson

Durant

, et al. Association of functional and structural social support with medication adherence among individuals treated for coronary heart disease risk factors: Findings from the REasons for Geographic and Racial Differences in Stroke (REGARDS) study. PloS one 2018; 13: e0198578. https://doi.org/10.1371/journal.pone.0198578

62.

Ehmann

Hagerman

Milliron

B-J

, et al. The role of household social support and undermining in dietary change. International journal of behavioral medicine 2024; 1–10. https://doi.org/10.1007/s12529-024-10327-w

63.

Conklin

Forouhi

Surtees

, et al. Social relationships and healthful dietary behaviour: evidence from over-50s in the EPIC cohort, UK. Social science & medicine 2014; 100: 167–175. https://doi.org/10.1016/j.socscimed.2013.08.018

64.

Sun

Song

, et al. The impact of emotional support on active aging among the elderly in China. Acta Psychologica 2024; 250: 104531. https://doi.org/10.1016/j.actpsy.2024.104531

65.

Cong

Huo

Jiang

, et al. Factors associated with the level of self-management in elderly patients with chronic diseases: a pathway analysis. BMC geriatrics 2024; 24: 377. https://doi.org/10.1186/s12877-024-04956-9

66.

Eller

Lev

Yuan

, et al. Describing self‐care self‐efficacy: Definition, measurement, outcomes, and implications. International journal of nursing knowledge 2018; 29: 38–48. https://doi.org/10.1111/2047-3095.12143

67.

Sheng

Doyle

Bond

, et al. Home-based digital health technologies for older adults to self-manage multiple chronic conditions: a data-informed analysis of user engagement from a longitudinal trial. Digital health 2022; 8: 20552076221125957. https://doi.org/10.1177/20552076221125957

68.

Yang

Gao

Jiang

. Digital divide as a determinant of health in the US older adults: prevalence, trends, and risk factors. BMC geriatrics 2024; 24: 1027. https://doi.org/10.1186/s12877-024-05612-y

69.

Wang

, et al. Social isolation in relation to the incidence and dynamic progression of frailty in the oldest old: a trajectory analysis of a nationwide cohort. BMC Public Health 2025; 25: 1809. https://doi.org/10.1186/s12889-025-22596-5

70.

Zhu

Wan

, et al. Older adults’ self-perception, technology anxiety, and intention to use digital public services. BMC Public Health 2024; 24: 3533. https://doi.org/10.1186/s12889-024-21088-2

71.

Mao

Tian

Zhang

, et al. Associations between social support and frailty and the mediating role of mental and physical health: evidence from CHARLS. BMC geriatrics 2025; 25: 384. https://doi.org/10.1186/s12877-025-06025-1

72.

Dutt

Webber

. Access to social capital and social support among South East Asian women with severe mental health problems: a cross-sectional survey. International Journal of Social Psychiatry 2010; 56: 593–605. https://doi.org/10.1177/0020764009106415

73.

Shiell

Hawe

Kavanagh

. Evidence suggests a need to rethink social capital and social capital interventions. Social science & medicine 2020; 257: 111930. https://doi.org/10.1016/j.socscimed.2018.09.006

74.

Steinman

Chadwick

Santos

, et al. Remote evidence-based programs for health promotion to support older adults during the COVID-19 pandemic and beyond: mixed methods outcome evaluation. JMIR aging 2024; 7: e52069. https://doi.org/10.2196/52069

75.

Shirvani

Ghaffari

Fotokian

, et al. Association between perceived family social support and self-care behaviors in elders with chronic obstructive pulmonary disease (COPD): a medical center-based study from Iran. The Open Nursing Journal 2020; 14: 1–7.

76.

SFV

Hsieh

Lin

, et al. Prediction of self‐care behaviour on the basis of knowledge about chronic kidney disease using self‐efficacy as a mediator. Journal of clinical nursing 2016; 25: 2609–2618. https://doi.org/10.1111/jocn.13305

77.

Barsell

Everhart

Miadich

, et al. Examining health behaviors, health literacy, and self-efficacy in college students with chronic conditions. American Journal of Health Education 2018; 49: 305–311. https://doi.org/10.1080/19325037.2018.1486758

78.

Han

Chu

Song

, et al. Social capital, socioeconomic status and self-efficacy. Applied Economics and Finance 2015; 2: 1–10. https://doi.org/10.11114/aef.v2i1.607

79.

Green

Van Horn

Gupte

, et al. Assessment of adaptive engagement and support model for people with chronic health conditions in online health communities: combined content analysis. Journal of Medical Internet Research 2020; 22: e17338. https://doi.org/10.2196/17338

80.

Chen

Zhang

, et al. Family care and health-promoting behaviors among community elders: The chain mediating effect of self-efficacy and aging expectations. Geriatric Nursing 2025; 66: 103606. https://doi.org/10.1016/j.gerinurse.2025.103606

81.

Chiarelli

Batistoni

SST

. An analysis of socioemotional selectivity theory in the context of older adults’ use of Facebook. Educational Gerontology 2021; 47: 13–24. https://doi.org/10.1080/03601277.2020.1849956

82.

Mahmood

Ahuja

Kedia

, et al. Patient-Centered Communication and Cancer Information Overload Among Adults without a History of Cancer in the United States. Journal of Health Communication 2025; 30: 133–145. https://doi.org/10.1080/10810730.2025.2465491

83.

Sharkiya

. Quality communication can improve patient-centred health outcomes among older patients: a rapid review. BMC Health Services Research 2023; 23: 886. https://doi.org/10.1186/s12913-023-09869-8

84.

Derksen

Bensing

Lagro-Janssen

. Effectiveness of empathy in general practice: a systematic review. The British Journal of General Practice 2012; 63: e76–e84. https://doi.org/10.3399/bjgp13X660814

85.

Park

Kim

E-J

Park

, et al. Digital Health Intervention Effect on Older Adults With Chronic Diseases Living Alone: Systematic Review and Meta-Analysis of Randomized Controlled Trials. Journal of Medical Internet Research 2025; 27: e63168. https://doi.org/10.2196/63168

86.

Peimani

Stewart

Ghodssi-Ghassemabadi

, et al. The moderating role of e-health literacy and patient-physician communication in the relationship between online diabetes information-seeking behavior and self-care practices among individuals with type 2 diabetes. BMC Primary Care 2024; 25: 442. https://doi.org/10.1186/s12875-024-02695-9

87.

Liu

Yeo

TED

. Patient-Centered Communication and Mental Health of Sexual Minority Adults: A Moderated Mediation Model. Health Communication 2025; 40: 1–12. https://doi.org/10.1080/10410236.2025.2476791

88.

Organization WH . World report on ageing and health. World Health Organization, 2015.

89.

Jeste

Blazer

First

. Aging-related diagnostic variations: need for diagnostic criteria appropriate for elderly psychiatric patients. Biological psychiatry 2005; 58: 265–271. https://doi.org/10.1016/j.biopsych.2005.02.004

90.

Bowman

Lennox

Clugston

, et al.

Breast reconstruction in older women: should age be an exclusion criterion?

Plastic and reconstructive surgery 2006; 118: 16–22. https://doi.org/10.1097/01.prs.0000220473.94654.a4

91.

Elixhauser

Steiner

Harris

, et al. Comorbidity measures for use with administrative data. Medical care 1998; 36: 8–27. https://doi.org/10.1097/00005650-199801000-00004

92.

Abel

. Measuring health lifestyles in a comparative analysis: theoretical issues and empirical findings. Social science & medicine 1991; 32: 899–908. https://doi.org/10.1016/0277-9536(91)90245-8

93.

Hwang

. Promoting healthy lifestyles with aging: Development and validation of the Health Enhancement Lifestyle Profile (HELP) using the Rasch measurement model. The American Journal of Occupational Therapy 2010; 64: 786–795. https://doi.org/10.5014/ajot.2010.09088

94.

Walker

Sechrist

Pender

. The health-promoting lifestyle profile: development and psychometric characteristics. Nursing research 1987; 36: 76–81.

95.

Cohen

Wills

. Stress, social support, and the buffering hypothesis. Psychological bulletin 1985; 98: 310–357.

96.

Smith

Levonian

, et al. “I Cannot Do All of This Alone” Exploring Instrumental and Prayer Support in Online Health Communities. ACM Transactions on Computer-Human Interaction (TOCHI) 2020; 27: 1–41. https://doi.org/10.1145/3402855

97.

Sherbourne

Stewart

. The MOS social support survey. Social science & medicine 1991; 32: 705–714. https://doi.org/10.1016/0277-9536(91)90150-b

98.

Price

Surr

Gough

, et al. The Provision of Social Support in an Online Support Forum for Caregivers of People With Comorbid Dementia and Cancer: Content Analysis Study. JMIR cancer 2025; 11: e72217. https://doi.org/10.2196/72217

99.

Agrawal

DaSouza

Mulgund

, et al. The Role of Online Support, Caregiving, and Gender in Preventative Cancer Genetic Testing Participation: Cross-Sectional Study From a National Study. JMIR cancer 2025; 11: e67650. https://doi.org/10.2196/67650

100.

Wheldon

Shahsavar

Choudhury

, et al. Loneliness among adult cancer survivors in the United States: prevalence and correlates. Scientific Reports 2025; 15: 3914. https://doi.org/10.1038/s41598-025-85126-8

101.

Holman

Lorig

. Perceived self-efficacy in self-management of chronic disease. In: Maddux

, editor. Self-efficacy. New York: Springer; 2014. p. 305–324.

102.

Smith

Wallston

Smith

. The development and validation of the Perceived Health Competence Scale. Health education research 1995; 10: 51–64. https://doi.org/10.1093/her/10.1.51

103.

Wallston

Rothman

Cherrington

. Psychometric properties of the perceived diabetes self-management scale (PDSMS). Journal of behavioral medicine 2007; 30: 395–401. https://doi.org/10.1007/s10865-007-9110-y

104.

McCormack

Treiman

Rupert

, et al. Measuring patient-centered communication in cancer care: a literature review and the development of a systematic approach. Social science & medicine 2011; 72: 1085–1095. https://doi.org/10.1016/j.socscimed.2011.01.020

105.

Epstein

Franks

Fiscella

, et al. Measuring patient-centered communication in patient–physician consultations: theoretical and practical issues. Social science & medicine 2005; 61: 1516–1528. https://doi.org/10.1016/j.socscimed.2005.02.001

106.

Epstein

Street

JRL

. Patient-centered communication in cancer care: promoting healing and reducing suffering, 2007.

107.

Jiang

. How does patient-centered communication improve emotional health? An exploratory study in China. Asian Journal of Communication 2018; 28: 298–314. https://doi.org/10.1080/01292986.2017.1413402

108.

Lai

Zhao

. Counteracting cyberchondria in Chinese chronic disease patients: the divergent roles of health-related social media use and online patient-centered communication. Patient Education and Counseling 2025; 141: 109337. https://doi.org/10.1016/j.pec.2025.109337

109.

Lang

Carstensen

. Close emotional relationships in late life: further support for proactive aging in the social domain. Psychology and aging 1994; 9: 315–324. https://doi.org/10.1037//0882-7974.9.2.315

110.

Comijs

Penninx

Knipscheer

, et al. Psychological distress in victims of elder mistreatment: The effects of social support and coping. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences 1999; 54: P240–P245. https://doi.org/10.1093/geronb/54b.4.p240

111.

Al-Dwaikat

Rababah

Al-Hammouri

, et al. Social support, self-efficacy, and psychological wellbeing of adults with type 2 diabetes. Western Journal of Nursing Research 2021; 43: 288–297. https://doi.org/10.1177/0193945920921101

112.

Kroenke

Spitzer

Williams

, et al. An ultra-brief screening scale for anxiety and depression: the PHQ–4. Psychosomatics 2009; 50: 613–621. https://doi.org/10.1176/appi.psy.50.6.613

113.

Apenteng

Hansen

Opoku

, et al. Racial disparities in emotional distress among cancer survivors: insights from the Health Information National Trends Survey (HINTS). Journal of Cancer Education 2017; 32: 556–565. https://doi.org/10.1007/s13187-016-0984-7

114.

Shim

Rust

. Health care avoidance among people with serious psychological distress: analyses of 2007 Health Information National Trends Survey. Journal of health care for the poor and underserved 2012; 23: 1620–1629. https://doi.org/10.1353/hpu.2012.0189

115.

Marcoulides

Raykov

. Evaluation of variance inflation factors in regression models using latent variable modeling methods. Educational and psychological measurement 2019; 79: 874–882. https://doi.org/10.1177/0013164418817803

116.

Kyriazos

Poga

. Dealing with multicollinearity in factor analysis: the problem, detections, and solutions. Open Journal of Statistics 2023; 13: 404–424. https://doi.org/10.4236/ojs.2023.133020

117.

Hayes

. Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. Guilford publications, 2017.

118.

Bolin

. Introduction to mediation, moderation, and conditional process analysis: a regression-based approach. JSTOR, 2014.

119.

Belsley

Kuh

Welsch

. Regression diagnostics: Identifying influential data and sources of collinearity. John Wiley & Sons, 2005.

120.

Yuan

S-C

Weng

S-C

Chou

M-C

, et al. How family support affects physical activity (PA) among middle-aged and elderly people before and after they suffer from chronic diseases. Archives of Gerontology and Geriatrics 2011; 53: 274–277. https://doi.org/10.1016/j.archger.2010.11.029

121.

Tian

, et al. The role of self-efficacy and self-care agency as mediating factors in the link between health literacy and health-promoting lifestyle among older adults post covid 19 era: A multiple mediator model. Geriatric Nursing 2023; 54: 252–257. https://doi.org/10.1016/j.gerinurse.2023.10.013

122.

Ong-Artborirak

Seangpraw

Boonyathee

, et al. Health literacy, self-efficacy, self-care behaviors, and glycemic control among older adults with type 2 diabetes mellitus: a cross-sectional study in Thai communities. BMC geriatrics 2023; 23: 297. https://doi.org/10.1186/s12877-023-04010-0

123.

Mills

Drury

Symons

, et al. Online support groups, social identity, and the health and wellbeing of adults with Long Covid: An interview study. Journal of Community & Applied Social Psychology 2024; 34: e2849. https://doi.org/10.1002/casp.2849

124.

Simoni

Franks

Lehavot

, et al. Peer interventions to promote health: conceptual considerations. American Journal of Orthopsychiatry 2011; 81: 351–359. https://doi.org/10.1111/j.1939-0025.2011.01103.x

125.

Guo

Tang

, et al. The relationship between illness uncertainty and social support among cancer patients: A meta-analysis. Cancer Nursing 2024; 10: 1097.

126.

Lieberman

Schroeder

. Two social lives: How differences between online and offline interaction influence social outcomes. Current opinion in psychology 2020; 31: 16–21. https://doi.org/10.1016/j.copsyc.2019.06.022

127.

Tsai

H-yS

Shillair

Cotten

. Social support and “playing around” an examination of how older adults acquire digital literacy with tablet computers. Journal of Applied Gerontology 2017; 36: 29–55. https://doi.org/10.1177/0733464815609440

128.

White

Nguyen

Roger

, et al. Tailoring communication practices to support effective delivery of telehealth in general practice. BMC Primary Care 2024; 25: 232. https://doi.org/10.1186/s12875-024-02441-1

129.

Treadgold

Coulson

Campbell

, et al. Quality and Misinformation About Health Conditions in Online Peer Support Groups: Scoping Review. Journal of Medical Internet Research 2025; 27: e71140. https://doi.org/10.2196/71140

130.

Hogan

Luger

Volkman

, et al. Patient centeredness in electronic communication: evaluation of patient-to-health care team secure messaging. Journal of medical Internet research 2018; 20: e82. https://doi.org/10.2196/jmir.8801

131.

King

Hoppe

. “Best practice” for patient-centered communication: a narrative review. Journal of graduate medical education 2013; 5: 385–393. https://doi.org/10.4300/JGME-D-13-00072.1

132.

Ling

Zheng

. How online Patient-Provider communication alleviates psychological distress among patients with chronic diseases: the role of perceived Patient-Centered communication and adaptive coping strategies. Health Communication 2025; 40: 1559–1569. https://doi.org/10.1080/10410236.2024.2411471

133.

Deshpande

Kelly

, et al. Video-based educational interventions for patients with chronic illnesses: systematic review. Journal of medical Internet research 2023; 25: e41092. https://doi.org/10.2196/41092

134.

Wang

Zhang

, et al. From Passive to Active—Improving the Healthy Self-Help Behavior of Older Adults Through Community Health Association: Mixed Methods Study. Journal of Medical Internet Research 2025; 27: e81062. https://doi.org/10.2196/81062

135.

Ben Liu

Liu

Guo

. The effects of participating in a physician-driven online health community in managing chronic disease: evidence from two natural experiments. MIS quarterly 2020; 44: 391–420. https://doi.org/10.25300/misq/2020/15102

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.44 MB