Abstract
Background
Digital transformation in healthcare, compounded by the erosion of informal caregiving reservoirs due to low fertility, has widened disparities in access to medical services among older adults. Medical visit companion services (MVCS) offer a promising solution. This study aims to explore the key determinants and hierarchical pathways influencing older adults’ willingness to use MVCS.
Methods
This study employed a quantitative cross-sectional design. A multistage stratified sampling method was used to recruit older adults in Zhejiang Province, China, from January to May 2024. Drawing on Andersen’s behavioral model, this study identified the determinants of older adults’ willingness to use MVCS through non-parametric tests and ordered logistic regression. Shapley value decomposition and interpretative structural modeling (ISM) were employed to quantify contributions and delineate the hierarchical mechanisms of influence.
Results
The study included 494 older adults, with a mean score for willingness to use MVCS of 3.27 ± 1.23. Shapley analysis revealed that need factors (45.97%), enabling resources (30.01%), and predisposing characteristics (24.02%) collectively explained variance in utilization willingness. ISM analysis revealed a three-tier hierarchical structure: (1) direct factors, including individual accompaniment demands and need for assistance from MVCS; (2) indirect factors, such as medical visit autonomy, self-rated health status, self-rated ease of medical visits, social support, and MVCS awareness; and (3) deep-rooted factors, comprising age, education, and number of children. These elements form a hierarchical path progressing from predisposing characteristics through enabling resources and evaluated needs, to perceived needs and, ultimately, utilization willingness.
Conclusions
Older adults’ willingness to use MVCS is primarily driven by perceived needs, which are structurally shaped by indirect enabling resources and deep-rooted predisposing characteristics. To effectively enhance this willingness, interventions should leverage the institutional authority of hospitals and community centers, facilitating the transition from general awareness to explicit utilization willingness. We recommend transforming MVCS from basic accompaniment to specialized medical navigation services to augment perceived service utility. Furthermore, policy efforts should prioritize precise resource allocation for vulnerable populations, particularly those characterized by poor health and diminished informal support from fewer children.
Supplementary Information
The online version contains supplementary material available at https://doi.org/10.1186/s12877-026-07774-3.
Keywords: Older adult, Medical visit companion services utilization, Andersen’s behavioral model, Shapley value method, Interpretative structural modeling, Influencing factors
Background
According to the China Statistical Yearbook 2024, individuals aged 60 and older account for 22.0% of the population, surpassing the World Health Organization threshold defining an “aged society” [1]. This demographic shift has substantially increased the demand for healthcare services. In China, there is a marked mismatch between the allocation of healthcare resources and regional needs. High-quality medical resources are disproportionately concentrated in major medical centers and urban areas, whereas primary healthcare institutions and rural regions face severe shortages [2]. This imbalance is associated with increased challenges for older adults seeking high-quality healthcare [3]. Digital healthcare services have played a pivotal role in addressing these challenges and supporting the goal of “medical care for the aged” [4]. However, the implementation of digital healthcare services faces numerous challenges. Older adults constitute a digitally marginalized population, encountering substantial cognitive and physical impediments to accessing technology-mediated healthcare services. Internet penetration rates among older adults in countries such as China, Mexico, and the United States remain suboptimal [5]. They also experience high cognitive loads and physical limitations when engaging with digital healthcare services [6]. This limits older adults’ access to digital healthcare and exacerbates healthcare inequalities [7]. To access healthcare services, older adults often rely on external assistance, particularly from adult children [8]. However, such support networks are frequently inadequate. Research based on the US Census and the University of Michigan Health and Retirement Study has shown that approximately 22% of adults aged 65 and older lack accessible kin, a legally designated surrogate, or a caregiver [9]. Similar patterns are observed in China [10]. Against this backdrop, as these countries face both declining fertility rates and shrinking household sizes, the inevitable depletion of informal care resources creates a widening gap in meeting older adults’ medical companionship needs [11, 12]. Moreover, the stress and anxiety associated with solitary medical visits can significantly impair the physical and mental health of older adults [13]. Addressing medical visit companionship for older adults has become a global public health priority that requires collaboration across families, governments, and societies.
Medical companionship services originated from the US Patient Navigation (PN) model [14], which was originally designed as an individualized intervention to support cancer patients. The primary objective of PN is to mitigate healthcare barriers, thereby ensuring timely access to essential diagnostic and treatment services. The model has gradually expanded to encompass various chronic diseases [15]. Within the Chinese healthcare system, it has undergone significant adaptation, ultimately evolving into medical visit companion services (MVCS). Unlike the PN model, MVCS function as a substitute for family accompaniment during medical visits. In China, this service is emerging as a response to the escalating demand for long-term care and as a strategic mechanism to bridge gaps in healthcare access [16, 17]. Specifically, MVCS serve as a whole-process support service, extending beyond basic physical assistance. They encompass pre-visit preparation (e.g., appointment scheduling, hospital selection, and transportation), in-visit navigation (e.g., navigating departments, retrieving medications, facilitating doctor-patient communication, and offering emotional assistance), and post-visit coordination (e.g., follow-up scheduling, medication guidance, and family feedback) [18–20]. However, the broader adoption of the service ultimately depends on the older adults’ willingness to utilize it. Understanding this willingness is essential for ensuring sustainable and scalable service delivery.
Existing literature has focused on adult children’s willingness to purchase MVCS for their parents [21, 22]. This proxy-respondent perspective may marginalize older adults’ autonomy, potentially leading to a misalignment between service provision and their lived experiences. While some qualitative studies have explored this issue from the perspective of older adults, they have largely relied on qualitative approaches [16, 17, 23, 24]. The lack of quantitative evidence necessitates further research to empirically validate these qualitative insights on a broader scale. A quantitative study utilizing the theory of planned behavior and the technology acceptance model confirmed that subjective perceptions significantly affect older adults’ willingness [25]. However, these frameworks predominantly emphasize psychological constructs while overlooking the enabling resources and need factors that are critical for older adults. Consequently, a more comprehensive framework is required to capture the complexities of service utilization. Andersen’s behavioral model offers a more holistic lens by integrating individual predispositions with enabling resources and actual needs, which are often the primary drivers for older adults. Previous studies suggest these dimensions are intercorrelated rather than mutually exclusive [26]. However, existing research often treats the specific factors within these dimensions as independent variables. This fails to account for the internal logical structure and hierarchical interactions between different levels, thereby obscuring the underlying influence pathways.
Therefore, based on Andersen’s behavioral model, this study will conduct a field survey to investigate the factors influencing older adults’ willingness to use MVCS. To further elucidate the relative importance of these determinants and their internal mechanisms, this study employs Shapley value decomposition to quantify factor contributions and utilizes Interpretative Structural Modeling (ISM) to delineate their hierarchical structure [27, 28]. The findings can inform the development and optimization of MVCS, offering valuable insights for international contexts with similar aging and healthcare accessibility issues.
Methods
Data sources
A quantitative cross-sectional study was conducted among older adults in Zhejiang Province, China, between January and May 2024. A multistage stratified sampling approach was employed. First, six cities in Zhejiang Province were selected by stratified random sampling based on geographic region and socioeconomic development indicators (high, medium, and low). Second, communities within each selected city were stratified into three socioeconomic tiers based on average property market value (serving as a proxy for household economic status) and public green spaces (reflecting infrastructure quality and livability for older adults). One community was randomly selected from each tier, totaling 18 communities. Finally, 25–30 older adults were recruited from each community using convenience sampling. Trained research assistants established on-site recruitment booths at community centers or community health service centers, making face-to-face contact with eligible older adults for questionnaire administration and guidance. Inclusion criteria for participants were: (1) Age ≥ 60 years; (2) Able to communicate independently and complete the questionnaire; (3) Provided written informed consent. Exclusion criteria included: (1) Diagnosis of mental disorders that preclude normal communication; (2) Refusal to participate.
The cross-sectional sample size was calculated using the standard formula: 
[29]. Due to the lack of prior literature, the expected prevalence of willingness to use MVCS was conservatively estimated at 0.5. With the allowable error of 0.05, the initial sample size was calculated as 385. The target sample size was increased to 462 to account for a 20% attrition rate. All investigators received standardized training before data collection to ensure consistent survey administration. After verifying eligibility and obtaining informed consent, trained volunteers or family members assisted illiterate older adults or those who had difficulty completing questionnaires during face-to-face interviews. To ensure data quality, questionnaires with missing data exceeding 10% were excluded from the final analysis.
Measurements
Theoretical model
This study was grounded in Andersen’s behavioral model, which conceptualizes service utilization as the result of predisposing characteristics, enabling resources, and need factors. Predisposing characteristics reflect individuals’ baseline tendencies and attitudes toward service use before the emergence of concrete needs, including demographics, social structure, and health beliefs [26]. Regarding enabling resources, the model emphasizes how individual, family, and community resources influence healthcare access [26]. However, high-quality medical resources in China are not distributed evenly on a community basis. Community resources alone cannot fully reflect the accessibility of core healthcare for older adults [2]. Furthermore, in the Chinese context, social support from family and peers exhibits distinct cross-regional characteristics that extend beyond the community [30]. Therefore, this study replaces community resources with a more inclusive dimension of social resources. Need factors capture both perceived and evaluated health-related demands that directly motivate utilization [31]. Accordingly, each variable in this study was theoretically assigned to one of these three dimensions based on its primary role in influencing older adults’ willingness to use MVCS. The conceptual framework and specific indicators used in this study are illustrated in Supplementary Fig. 2.
Variable measurement
Outcome variable Willingness to use MVCS was operationalized as the participants’ subjective behavioral intention to use the service [22]. This was assessed using a single-item measure on a 5-point Likert scale. Participants were asked to rate their degree of willingness, with response options ranging from 1 (“strongly unwilling”) to 5 (“strongly willing”).
Independent variables
Predisposing characteristics were categorized into demographics, social structure, and health beliefs. These variables comprised age (60–69, 70–79, and ≥ 80), sex (male and female), education (primary school or below, secondary school, high school, junior college, and higher education) marital status (never married or divorced, married, and widowed), residential status (live alone, live with a spouse, live with adult children, live with both spouse and adult children), and number of children (0, 1, 2, ≥ 3). The perceived development prospects of MVCS were scored on a 5-point Likert scale.
Enabling resources were categorized into family/personal factors and community factors, including financial capacity, informational access, family and social support, and functional autonomy in navigating medical visits. Financial capacity was measured using monthly family income (RMB), categorized as < 2000, 2001–4000, 4001–6000, 6001–8000, and > 8000. Informational access was assessed through MVCS awareness (1 = completely unclear, 5 = very clear). Functional autonomy and accessibility were captured by self-rated ease of medical visits (1 = very poor, 5 = very good) and medical visit autonomy (yes/no). Family support was assessed using the Chinese version of the Family APGAR Index (APGAR) [32], which contains five items on a 3-point Likert scale. Total scores range from 0 to 10, classified into low (0–3), moderate (4–6), and high (7–10) levels of family support. Cronbach’s α in this study was 0.743. Social support was measured using the Social Support Rating Scale (SSRS) [33], a 10-item instrument covering objective support, subjective support, and support utilization. Total scores range from 12 to 66, classified into poor (≤ 22), moderate (23–44), and adequate (45–66). Cronbach’s α coefficient in this study was 0.735.
Need factors were categorized into evaluated needs and perceived needs. The evaluated needs were evaluated using self-rated health status (rated from 5 “very good” to 1 “very poor”) and the presence of chronic diseases (yes/no). Perceived needs were captured through individual accompaniment demands (measured on a 5-point Likert scale) and the need for assistance from MVCS. The need for assistance from MVCS questionnaire was measured using a structured questionnaire developed through a mixed-method process. An initial pool of 20 items was generated based on a literature review [19, 20, 23, 24] and qualitative interviews [17]. A Delphi process involving 12 geriatric medicine specialists was conducted to refine the instrument. After two rounds of consultation, 15 items were retained. The final scale covers three dimensions, including in-hospital medical visit support (8 items), medical visit path optimization (4 items), and service quality assurance (3 items). All items were rated on a 5-point Likert scale (1 = strongly no demand, 5 = strongly demand). To minimize optimism bias, the sample was randomly divided into two independent subsamples (n = 247 each) for exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) respectively. EFA showed an improved KMO of 0.913 and a Bartlett’s test χ² = 3524.472 (P < 0.001), explaining 63.25% of the total variance. CFA confirmed χ² /df = 2.112, RMSEA = 0.073, GFI = 0.921, CFI = 0.904, TLI = 0.941, and NFI = 0.906. The scale-level content validity index was 0.899, with the item-level content validity indices ranging from 0.832 to 1.000. The instrument demonstrated high reliability, with Cronbach’s α of 0.917 for the total scale and values ranging from 0.812 to 0.892 for the three dimensions. Furthermore, a test-retest reliability coefficient of 0.838 was obtained after a two-week interval.
Statistical method
Data were analyzed using SPSS 27.0 and Stata 18.0. For the initial stage, descriptive statistics were presented as frequencies and percentages for categorical variables, and as means with standard deviations for continuous variables. Regarding group comparisons, the Chi-square tests (χ2) were employed for categorical variables, whereas the Kruskal-Wallis H tests (H) were used for continuous variables, given their non-normal distribution. Furthermore, Spearman correlation and Multicollinearity tests (ensuring variance inflation factors < 5 ) [34] were conducted to identify predictors and ensure model stability (Supplementary Table 2).
Given the ordinal nature of the outcome variable, cumulative odds model was fitted to analyze the data. The proportional odds assumption for this model was tested using the parallel lines test (p > 0.05). This model was specified as follows: 
, where y represents willingness to use MVCS, X1, X2, X3 represent predisposing characteristics, enabling resources, and need factors, respectively. Model’s fit was assessed using the chi-square goodness-of-fit test and the pseudo-R² statistic. The variable selection for this model was guided by Andersen’s behavioral model as the theoretical framework. While maintaining coverage across predisposing characteristics, enabling resources, and need factors, this study identified and incorporated 3 to 4 factors with statistical significance (P < 0.05) from each category. These variables were then incorporated into the final model using a backward stepwise regression method. Multi-categorical nominal variables were entered as dummy-coded variables, while ordinal predictors were treated as continuous scores. Specific assignments and coding are detailed in Supplementary Table 3. To further quantify the impact of these factors, Shapley value decomposition (via the shapley2 command in Stata) was applied [35].
To move beyond linear associations and explore the hierarchical interrelationships between determinants, Interpretative Structural Modeling (ISM) was utilized. A panel of 13 experts (7 senior, 6 intermediate) in geriatric nursing, public health, and social work was consulted to establish the logical pathways between factors. The analytical process involved five stages [28]: (1) extracting significant factors from the regression results; (2) construction of the adjacency matrix to represent the direct relationships between factors; (3) calculating the reachability matrix to determine the indirect pathways; (4) decomposing the hierarchical levels; (5) visualizing the multi-level structural model to delineate the deep-rooted pathways influencing the utilization willingness of older adults.
Results
Sample characteristics
A total of 510 participants were recruited, of whom 494 provided valid responses after excluding incomplete questionnaires, yielding an effective response rate of 96.86%. The mean age of the participants was 68.38 (SD = 7.15) years. In total, 51.5% of participants reported good or very good self-rated health, and 30.8% fell within the 6001–8000 yuan monthly household income category. In terms of family and social support, 44.3% had two children. The mean scores for social support and family care were 38.71 (SD = 5.74) and 7.66 (SD = 2.02), respectively. Regarding healthcare utilization, 51.2% were able to complete medical visits independently, and 40.1% perceived their medical visit as convenient. With respect to MVCS, 67.8% reported being unclear or completely unclear about such services. Nevertheless, 79.6% expressed a need for personalized accompaniment. Detailed participants’ characteristics are presented in Table 1.
Table 1.
The sociodemographic characteristics of participants (n = 494)
| Variables | Total (n = 494) |
|---|---|
| Predisposing characteristics | |
| Age (years) | |
| 60–69 | 280 (56.7) |
| 70–79 | 163 (33.0) |
| 80~ | 51 (10.3) |
| Sex | |
| Male | 254 (51.4) |
| Female | 240 (48.6) |
| Education level | |
| Primary school or below | 234 (47.4) |
| Secondary school | 146 (29.6) |
| High school | 67 (13.6) |
| Junior college | 33 (6.7) |
| Higher education | 14 (2.8) |
| Marital status | |
| Never married or divorced | 15 (3.0) |
| Married | 430 (87.0) |
| Widowed | 49 (9.9) |
| Residential status | |
| Live alone | 52 (10.5) |
| Live with a spouse | 250 (50.6) |
| Live with adult children | 82 (16.6) |
| Live with both a spouse and adult children | 110 (22.3) |
| Number of children | |
| 0 | 6 (1.2) |
| 1 | 186 (37.7) |
| 2 | 219 (44.3) |
| ≥ 3 | 83 (16.8) |
| Enabling resources | |
| Self-rated ease of medical visits | |
| Very convenient | 124 (25.1) |
| Convenient | 198 (40.1) |
| Average | 103 (20.9) |
| Inconvenient | 52 (10.5) |
| Very inconvenient | 17 (3.4) |
| MVCS awareness | |
| Very clear | 10 (2.0) |
| Clear | 73 (14.8) |
| Moderate | 76 (15.4) |
| Unclear | 195 (39.5) |
| Completely unclear | 140 (28.3) |
| Social support | |
| Low (≤22) | 4 (0.8) |
| Moderate (23-44) | 418 (84.6) |
| High (≥45) | 72 (14.6) |
| Family care | |
| Low (0-3) | 17 (3.4) |
| Moderate (4-6) | 116 (23.5) |
| High (7-10) | 361 (73.1) |
| Monthly family income in RMB | |
| ≤ 2000 | 65 (13.2) |
| 2001-4000 | 93 (18.8) |
| 4001-6000 | 140 (28.3) |
| 6001-8000 | 152 (30.8) |
| > 8000 | 44 (5.3) |
| Medical visits autonomy | |
| Yes | 253 (51.2) |
| No | 241 (48.8) |
| Need factors | |
| Individual accompaniment demands | |
| Strongly agree | 198 (40.1) |
| Agree | 195 (39.5) |
| General | 70 (20.2) |
| Disagree | 26 (5.3) |
| Strongly disagree | 5 (1.0) |
| Chronic conditions | |
| Yes | 329 (66.7) |
| No | 165 (33.4) |
| Self-rated health status | |
| Very good | 66 (13.4) |
| Good | 188 (38.1) |
| General | 143 (28.9) |
| Poor | 81 (16.4) |
| Very poor | 16 (3.2) |
| Need for assistance from MVCS | |
| Low (<35) | 33 (6.7) |
| Moderate (35-54) | 144 (29.1) |
| High (≥55) | 317 (64.2) |
| Perceived development prospects of MVCS | |
| Broad development potential | 92 (18.6) |
| Good development potential | 245 (49.6) |
| Moderate development potential | 118 (23.9) |
| Limited development potential | 28 (5.7) |
| No development potential | 11 (2.2) |
Willingness and need for MVCS
The overall willingness to use MVCS among older adults was 3.27 (SD = 1.23). Specifically, 258 (52.2%) participants were willing to use the service, while 196 (39.7%) were unwilling. The distribution of willingness scores is presented in Supplementary Fig. 1. Regarding the need for MVCS assistance, the mean score was 56.36 (SD = 12.13). The scores for specific need items are detailed in Supplementary Table 1.
Factors associated with the utilization of MVCS
Table 2 indicates that a combination of predisposing characteristics, enabling resources, and need factors significantly influenced older adults’ willingness to use MVCS. Among the predisposing characteristics, age, sex, education, and number of children were statistically significant factors (p < 0.05). Regarding enabling resources, social support, monthly family income, self-rated ease of medical visits, MVCS awareness, and family care were significantly associated with MVCS willingness (p < 0.05). Within the need factors, medical visits autonomy, the presence of chronic diseases, individual accompaniment demands, self-rated health status, the perceived need for MVCS, and expectation for MVCS development were statistically significant (p < 0.05).
Table 2.
Comparisons of willingness to use medical visit companion service (n = 494)
| Variables | Willingness score (Mean ± SD) |
χ² / H | p-value |
|---|---|---|---|
| Predisposing characteristics | |||
| Age (years) | |||
| 60–69 | 3.42 ± 1.22 | 22.545 | 0.004 |
| 70–79 | 3.20 ± 1.23 | ||
| 80~ | 2.71 ± 1.19 | ||
| Sex | |||
| Male | 3.21 ± 1.27 | 10.148 | 0.038 |
| Female | 3.34 ± 1.19 | ||
| Education | |||
| Primary school or below | 3.01 ± 1.22 | 54.515 | < 0.001 |
| Secondary school | 3.35 ± 1.18 | ||
| High school | 3.97 ± 1.11 | ||
| Junior college | 3.33 ± 1.19 | ||
| Higher education | 3.43 ± 1.28 | ||
| Marital status | |||
| Never married or divorced | 3.60 ± 1.18 | 7.405 | 0.494 |
| Married | 3.25 ± 1.24 | ||
| Widowed | 3.35 ± 1.20 | ||
| Residential status | |||
| Live alone | 3.52 ± 1.23 | 15.985 | 0.192 |
| Live with a spouse | 3.32 ± 1.23 | ||
| Live with adult children | 3.02 ± 1.25 | ||
| Live with both a spouse and adult children | 3.25 ± 1.22 | ||
| Number of children | |||
| 0 | 3.67 ± 1.03 | 47.385 | < 0.001 |
| 1 | 3.68 ± 1.23 | ||
| 2 | 3.10 ± 1.19 | ||
| ≥ 3 | 2.81 ± 1.11 | ||
| Enabling resources | |||
| Self-rated ease of medical visits | |||
| Very convenient | 2.88 ± 1.19 | 54.642a | < 0.001 |
| Convenient | 3.21 ± 1.19 | ||
| Average | 3.64 ± 1.22 | ||
| Inconvenient | 3.56 ± 1.27 | ||
| Very inconvenient | 3.76 ± 1.09 | ||
| MVCS awareness | |||
| Very clear | 4.40 ± 1.08 | 91.930a | < 0.001 |
| Clear | 3.86 ± 1.10 | ||
| Moderate | 3.51 ± 1.08 | ||
| Unclear | 2.98 ± 1.17 | ||
| Completely unclear | 3.16 ± 1.31 | ||
| Social support | |||
| Low (≤ 22) | 3.25 ± 1.50 | 22.847a | < 0.001 |
| Moderate (23–44) | 3.35 ± 1.23 | ||
| High (≥ 45) | 2.82 ± 1.14 | ||
| Family care | |||
| Low (0–3) | 3.29 ± 1.49 | 58.117a | < 0.001 |
| Moderate (4–6) | 3.72 ± 1.12 | ||
| High (7–10) | 3.13 ± 1.22 | ||
| Monthly family income in RMB | |||
| ≤ 2000 | 2.92 ± 1.29 | 28.577 | 0.027 |
| 2001–4000 | 3.31 ± 1.22 | ||
| 4001–6000 | 3.22 ± 1.14 | ||
| 6001–8000 | 3.34 ± 1.28 | ||
| > 8000 | 3.66 ± 1.22 | ||
| Medical visits autonomy | |||
| Yes | 3.15 ± 1.28 | 11.450 | 0.022 |
| No | 3.41 ± 1.17 | ||
| Need factors | |||
| Individual accompaniment demands | |||
| Strongly agree | 3.51 ± 1.25 | 120.688a | < 0.001 |
| Agree | 3.31 ± 1.14 | ||
| General | 2.84 ± 1.20 | ||
| Disagree | 2.69 ± 1.23 | ||
| Strongly disagree | 1.80 ± 1.79 | ||
| Self-rated health status | |||
| Very good | 2.95 ± 1.25 | 85.831a | < 0.001 |
| Good | 2.88 ± 1.17 | ||
| General | 3.42 ± 1.12 | ||
| Poor | 4.01 ± 1.16 | ||
| Very poor | 4.19 ± 0.91 | ||
| Chronic conditions | |||
| Yes | 3.37 ± 1.24 | 11.085 | 0.026 |
| No | 3.07 ± 1.21 | ||
| Need for assistance from MVCS | |||
| Low (<35) | 2.33 ± 1.32 | 42.739a | <0.001 |
| Moderate (35-54) | 2.94 ± 1.15 | ||
| High (≥55) | 3.52 ± 1.18 | ||
| Perceived development prospects of MVCS | |||
| Broad development potential | 4.12 ± 1.15 | 109.848a | <0.001 |
| Good development potential | 3.25 ± 1.16 | ||
| Moderate development potential | 2.87 ± 1.14 | ||
| Limited development potential | 2.50 ± 1.04 | ||
| No development potential | 2.91 ± 1.22 | ||
“a” represents Kruskal-Wallis H tests
Logistic regression analysis of willingness to use MVCS
Supplementary Table 4 presents the results of the cumulative odds models. Among predisposing characteristics, participants aged ≥ 80 years were significantly less likely to use MVCS compared with those aged 60–69 (OR = 0.325, 95%CI 0.169 ~ 0.623), while high school education was associated with higher willingness (OR = 3.592, 95%CI 2.066 ~ 6.243). Regarding enabling resources, willingness to use MVCS was positively associated with MVCS awareness (OR = 1.345, 95%CI 1.125 ~ 1.608) but negatively associated with self-rated ease of medical visits (OR = 0.711, 95%CI 0.598 ~ 0.846) and social support (OR = 0.933, 95%CI 0.903 ~ 0.963). For need factors, individualized accompaniment demands (OR = 1.418, 95%CI 1.133 ~ 1.774) and need for assistance from MVCS (OR = 1.043, 95%CI 1.025 ~ 1.061) were significantly associated with a higher likelihood of willingness. Conversely, medical visit autonomy (OR = 0.646, 95%CI 0.450 ~ 0.929) and better self-rated health were associated with lower willingness. Specifically, a one-unit increment in self-rated health score was associated with 38% lower odds of being willing to use MVCS (OR = 0.620, 95%CI 0.512 ~ 0.750).
Shapley value decomposition of factors associated with willingness to use MVCS
The Shapley value decomposition, based on Andersen’s behavioral model, identified the relative importance of its three theoretical dimensions to the variance in older adults’ willingness to use MVCS. Need factors were the most influential, accounting for 45.97% of the total variance, followed by enabling resources (30.01%) and predisposing characteristics (24.02%). Among the specific influencing factors, self-rated health status (21.28%), need for assistance from MVCS (15.56%), number of children (11.67%), MVCS awareness (9.40%), and individual accompaniment demands (9.13%) were identified as the most significant variables. Together, these factors explained the majority of the variance in willingness to use MVCS among older adults. Figure 1 provides a detailed breakdown of the contribution of each variable.
Fig. 1.
Shapley value decomposition results of factors influencing older adults’ willingness to use medical visit companion services. Note: The percentages represent the explanatory weight of each variable, where a higher percentage indicates a greater relative importance in influencing utilization willingness
Interpretative structural modeling of factors influencing the willingness to use MVCS
Figure 2 illustrates the hierarchical structure of older adults’ willingness to use MVCS. The results are organized into a three-level hierarchy, where predisposing characteristics influence enabling resources and evaluated needs, which in turn shape perceived needs and ultimately the willingness to use MVCS. The three-level hierarchy comprises: (1) the direct influencing layer, comprising perceived needs, such as the need for assistance from MVCS and individual accompaniment demands; (2) the indirect influencing layer, consisting of enabling resources and evaluated needs such as MVCS awareness, ease of medical visits, medical autonomy, social support, and self-rated health; and (3) the deep-rooted influencing layer, encompassing predisposing characteristics such as age, education, and number of children. Furthermore, based on the reachability matrix, the primary pathways driving older adults’ willingness to use MVCS are categorized into the following four hierarchical routes. Path1: age→MVCS awareness→need for assistance from MVCS or individual accompaniment demands→utilization willingness; Path2: age→self-rated health status→self-rated ease of medical visits or medical visit autonomy→need for assistance from MVCS or individual accompaniment demands→utilization willingness; Path3: education→MVCS awareness or self-rated ease of medical visits or medical visit autonomy→need for assistance from MVCS or individual accompaniment demands→utilization willingness; and Path4: number of children→social support→self-rated ease of medical visits→need for assistance from MVCS or individual accompaniment demands→utilization willingness.
Fig. 2.
Interpretative structural model of influencing factors of older adults’ willingness to use medical visit companion services. Note: Unidirectional arrows indicate direct driving effects, while bidirectional arrows represent mutual influence between factors. Path1 (pink path): age→MVCS awareness→need for assistance from MVCS or individual accompaniment demands→utilization willingness; Path2 (Green path): age→self-rated health status→self-rated ease of medical visits or medical visit autonomy→need for assistance from MVCS or individual accompaniment demands→utilization willingness; Path3 (Yellow path): education→MVCS awareness or self-rated ease of medical visits or medical visit autonomy→need for assistance from MVCS or individual accompaniment demands→utilization willingness; and Path4 (Dark blue path): number of children→social support→self-rated ease of medical visits→need for assistance from MVCS or individual accompaniment demands→utilization willingness. Light blue arrows denote shared pathways common to multiple hierarchical routes
Discussion
This study found that just over half of older adults expressed willingness to use MVCS, consistent with existing findings [36]. This willingness is lower than that reported among younger adults [37]. This disparity may reflect younger adults prioritizing time costs, while older adults emphasize economic costs. Driven by cost-saving considerations, older adults often adopt alternative strategies such as navigating medical visits independently, delaying necessary care, or relying on family accompaniment [16, 38]. This contrasts with other supportive services, such as patient navigation, which has demonstrated higher utilization willingness and documented benefits in overcoming financial, cultural, and logistical barriers to enhance treatment adherence [39, 40]. However, MVCS currently offers a limited functional scope, focused primarily on physical accompaniment, while lacking such broad systemic assistance. This may partly explain the relatively modest willingness observed and underscores the need to further examine the factors influencing older adults’ willingness to use MVCS.
Need factors emerged as the predominant dimension associated with older adults’ willingness to use MVCS, accounting for nearly half of the total explanatory variance. This aligns with prior research on emerging healthcare services [35, 41]. It suggests that willingness to utilize such emerging services may be more closely associated with need factors than with enabling resources. According to Andersen’s behavioral model, need factors encompass both evaluated and perceived needs [26]. Regarding perceived needs, individual accompaniment demands and the need for assistance from MVCS account for 24.69% of the total explanatory variance, underscoring the pivotal influencing role of perceived needs in shaping service utilization willingness. These factors are positively correlated with, and exert a direct impact on, the willingness to use MVCS. A failure to perceive needs for MVCS leaves the willingness to utilize the service latent. This deficit exerts a direct inhibitory effect on utilization willingness [42]. Within the evaluated needs, self-rated health status emerged as the most significant factor, while not exerting an immediate impact on utilization willingness. Instead, it exerts its ultimate impact on utilization willingness through Path2. This indicates that physical frailty alone does not automatically trigger the willingness to use MVCS. Health status functions as a latent driver, translating into willingness through the barriers encountered during medical visits [39]. Consequently, older adults in poor health may face formidable obstacles in navigating complex hospital environments, potentially fostering a pronounced perceived need for assistance. However, given potential discrepancies between self-rated and actual health status, this link warrants cautious interpretation alongside broader assessments of health literacy and functional ability [43]. Furthermore, this study found that chronic conditions significantly influenced older adults’ willingness to utilize the service, a finding that is consistent with previous research [25]. This may be attributed to the fact that long-term chronic conditions often lead to a decline in physical functioning and physiological frailty, resulting in a higher frequency of healthcare needs [44]. Nevertheless, it did not emerge as a significant predictor in this study, likely due to the fact that self-rated health status, as a comprehensive composite indicator, may have encompassed the perceived physical and psychological burden of chronic conditions, thereby absorbing its explanatory power within the model [45].
Enabling resources represent the structural conditions that may translate perceived needs into willingness [26]. Among these, service awareness emerged as a pivotal factor that facilitates utilization willingness by influencing perceived needs. As an emerging service, MVCS remains largely limited in terms of public visibility for many older adults [25]. This lack of awareness acts as a primary barrier, as older adults who are unfamiliar with the service’s functions fail to conceptualize MVCS as a viable solution to their clinical difficulties [46]. Existing studies suggest that regulatory ambiguity can foster safety concerns, potentially leading older adults to perceive such services with skepticism [17, 47]. This trust-based concern might further suppress the perceived utility of MVCS, thereby reducing their willingness. Similarly, self-rated ease of medical visits influences utilization willingness through the path of affecting perceived needs. The rapid digital transformation of healthcare and the increasing complexity of hospital environments have significantly diminished the convenience of medical visits for older adults [4]. Confronted with these barriers, older adults may develop a functional necessity for external assistance, which translates into their perceived need for MVCS. Additionally, social support directly influences self-rated ease of medical visits. Within the hierarchical structure, social support exerts its influence exclusively through this single pathway. Our findings reveal that older adults with stronger support networks exhibit lower willingness. When effective informal accompaniment is available, it significantly enhances the self-rated ease of medical visits, thereby fulfilling the individual’s assistance needs [48]. Consequently, this robust informal support system offsets the impetus to seek external professional assistance, directly weakening the perceived need for MVCS. This aligns with prior research indicating that older adults with high levels of social support exhibit a lower willingness to MVCS [25].
ISM analysis reveals that predisposing characteristics occupy the deep-rooted influence layer, acting as the fundamental factors that influence how other key variables impact utilization willingness. Regarding age, its influence on utilization willingness is primarily exerted through Path 1 and Path 2. Specifically, Path 2 illustrates that age influences self-rated health status, which in turn shapes perceived needs and ultimately determines utilization willingness. Advancing age typically correlates with declining physical health, which should stimulate perceived needs and elevate utilization willingness [49]. However, this study observes an inverse association between age and utilization willingness. This divergence is primarily attributable to Path 1, in which advancing age restricts MVCS awareness, thereby suppressing perceived needs and inhibiting utilization willingness. This trend may be partially interpreted through the lens of traditional cultural norms [49]. Previous literature suggests that in the Chinese context, reliance on formal services might be viewed as a lack of filial support, potentially creating a moral risk for children [50]. Although our study did not directly measure cultural values, it is plausible that such deep-seated norms might inhibit the translation of objective needs into explicit willingness. Furthermore, older adults may adopt a strategy of suppressing their medical needs to avoid burdening their families, a tendency which could subsequently inhibit their awareness and proactive engagement with MVCS [51]. This cultural barrier may potentially prevent latent needs from being translated into explicit utilization willingness. Additionally, the number of children influences service utilization through Path4. Under the traditional Chinese ethos of raising children to provide for old age, adult children remain the primary providers of medical visit assistance and the predominant source of social support [52]. However, the prevailing trend of declining fertility has severely eroded this informal care reservoir, leading to a structural deficit in family-based support. In the absence of child-based assistance, older adults manifest a significantly higher willingness to utilize professional medical accompaniment services.
Strengths and limitations
This study investigates the willingness of older adults in Zhejiang Province, China, to use MVCS and its key influencing factors, employing Shapley value analysis to clarify the contribution weights of each factor. Furthermore, ISM is utilized to reveal the complex interactions among factors, addressing the limitations of isolated factor analysis in previous research. The resulting hierarchical model elucidates the transmission paths and influencing logic between variables. This not only provides a comprehensive perspective for understanding the decision-making psychology of older adults but also offers a scientific basis for formulating intervention policies in the future.
This study has several limitations. First, it focused solely on older adults’ willingness to use MVCS, without incorporating their willingness to pay, which significantly influences actual utilization in a market-based context. Additionally, the exclusion of influencing factors such as perceived usefulness, trust, affordability, acceptability, and anticipated feasibility may limit the analysis of utilization willingness. Second, limitations exist concerning the measurement of service needs. Given the evolving nature of MVCS in China, this study may not have fully captured the breadth of potential service needs. Although the newly developed questionnaire underwent reliability and validity testing, its novelty implies the possibility of residual measurement error. Furthermore, reliance on self-reported data for key variables introduces susceptibility to response bias and social desirability bias. Third, the generalizability of our findings is limited by the geographic scope and sample size, as participants were drawn solely from Zhejiang Province, and the sample size was relatively small. Fourth, this study is limited by a relatively narrow selection of physical health indicators, lacking standardized clinical assessments such as frailty screening tools and activities of daily living scales. Fifth, this study did not include measures for cultural beliefs, filial piety norms, or trust in formal care. The absence of these metrics limits our ability to fully verify the cultural mechanisms that may underpin the observed associations. Lastly, the cross-sectional design precludes the establishment of causal relationships. This study cannot definitively infer a causal link between the influential factor and the outcome measures.
Conclusions
Older adults in China exhibit a moderate willingness to use MVCS. Need factors serve as the primary drivers, where perceived needs exert a direct influence, and evaluated needs provide an indirect influence on willingness. Furthermore, enabling resources and predisposing characteristics function as indirect and root influences, respectively. Since perceived need directly influences utilization willingness and is affected by other key factors, it serves as a critical factor for targeted interventions. First, interventions should prioritize directly enhancing the perceived need by leveraging the strategic leverage of increasing service awareness of increasing service awareness. Regarding promotional channels, high-trust hospitals and community centers should serve as primary nodes to establish service information points in areas with a high concentration of older populations. Leveraging medical personnel to provide professional guidance can effectively reduce information asymmetry and transform general awareness into specific perceived needs. Regarding service substance, service providers should shift from basic accompaniment to professional clinical assistance to enhance perceived needs through individualized solutions. Second, target populations with high latent demand should be identified. Interventions should prioritize vulnerable older adults characterized by poor health status, low perceived ease of medical visits, and insufficient informal support systems (e.g., those with fewer children). Identification of these individuals enables optimized resource allocation to those facing the highest barriers to healthcare access. Finally, while this study clarifies the drivers of willingness, the transition from willingness to actual uptake remains complex. Future research should address systematic barriers, including standardized pricing and cross-institutional service integration, to ensure the sustainable development of the MVCS market.
Supplementary Information
Acknowledgements
The authors thank all the subjects for their participation.
Authors’ contributions
CG, YC, and JX initiated the study. CG, YC, JX, JM, LZ, and DL contributed to its design. DL YC, and LT managed the data collection, with DL and YC managing data curation. JX, JM, and LZ performed the data analysis. JX wrote the first draft of the manuscript. CG and YC critically revised the paper. All authors reviewed critically subsequent drafts of the manuscript and approved the final manuscript.
Funding
This work was supported by the Planning Project of Zhejiang Provincial Philosophy and Social Science (Grant No.22NDQN253YB) and the National Social Science Foundation of China (Grant No. 24CSH134).
Data availability
The datasets generated and/or analysed during the current study are not publicly available due to concerns regarding participant privacy and confidentiality, but are available from the corresponding author on reasonable request.
Declarations
Ethics approval and consent to participate
All methods were performed in accordance with the Declaration of Helsinki. Participation was voluntary and after the provision of written informed consent. The data were anonymized, and participants were assured of confidentiality. Ethical approval was obtained from Wenzhou Medical University (NO.2023-002).
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Contributor Information
Yingying Chen, Email: 15167795126@163.com.
Chenchen Gao, Email: gaochenchen@wmu.edu.cn.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The datasets generated and/or analysed during the current study are not publicly available due to concerns regarding participant privacy and confidentiality, but are available from the corresponding author on reasonable request.